start-ver=1.4 cd-journal=joma no-vol=49 cd-vols= no-issue=4 article-no= start-page=291 end-page=297 dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240330 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluation of the trend of set-up errors during the treatment period using set-up margin in prostate radiotherapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=Accurate information on set-up error during radiotherapy is essential for determining the optimal number of treatments in hypofractionated radiotherapy for prostate cancer. This necessitates careful control by the radiotherapy staff to assess the patient's condition. This study aimed to develop an evaluation method of the temporal trends in a patient's specific prostate movement during treatment using image matching and margin values. This study included 65 patients who underwent prostate volumetric modulated arc therapy (mean treatment time, 87.2 s). Set-up errors were assessed using bone, inter-, and intra-fraction marker matching across 39 fractions. The set-up margin was determined by dividing the four periods into 39 fractions using Stroom's formula and correlation coefficient. The intra-fraction set-up error was biased in the anterior-superior (AS) direction during treatment. The temporal trend of set-up errors during radiotherapy slightly increased based on bone matching and inter-fraction marker matching, with a 1.6-mm difference in the set-up margin fractions 11 to 20. The correlation coefficient of the mean prostate movement during treatment significantly decreased in the superior-inferior direction, while remaining high in the left-right and anterior-posterior directions. Image matching contributed significantly to the improvement of set-up errors; however, careful attention is needed for prostate movement in the AS direction, particularly during short treatment times. Understanding the trend of set-up errors during the treatment period is essential in numerical information sharing on patient condition and evaluating the margins for tailored hypo-fractionated radiotherapy, considering the facility's image-guided radiation therapy technology. en-copyright= kn-copyright= en-aut-name=SasakiHinako en-aut-sei=Sasaki en-aut-mei=Hinako kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=MorishitaTakumi en-aut-sei=Morishita en-aut-mei=Takumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=IrieNaho en-aut-sei=Irie en-aut-mei=Naho kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KojimaRena en-aut-sei=Kojima en-aut-mei=Rena kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KiriyamaTetsukazu en-aut-sei=Kiriyama en-aut-mei=Tetsukazu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=NakamotoAkira en-aut-sei=Nakamoto en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NishiokaKunio en-aut-sei=Nishioka en-aut-mei=Kunio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=TakahashiShotaro en-aut-sei=Takahashi en-aut-mei=Shotaro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University Medical School kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University Medical School kn-affil= affil-num=3 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University Medical School kn-affil= affil-num=4 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University Medical School kn-affil= affil-num=5 en-affil=Department of Radiology, Uwajima City Hospital kn-affil= affil-num=6 en-affil=Department of Radiology, Tokuyama Central Hospital kn-affil= affil-num=7 en-affil=Department of Radiology, Tokuyama Central Hospital kn-affil= affil-num=8 en-affil=Department of Radiology, Tokuyama Central Hospital kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= en-keyword=Hypofractionated radiotherapy kn-keyword=Hypofractionated radiotherapy en-keyword=Image-guided radiation therapy kn-keyword=Image-guided radiation therapy en-keyword=Prostate cancer kn-keyword=Prostate cancer en-keyword=Prostate movement kn-keyword=Prostate movement en-keyword=Set-up margin kn-keyword=Set-up margin END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page= end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240516 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluation of output factors of different radiotherapy planning systems using Exradin W2 plastic scintillator detector en-subtitle= kn-subtitle= en-abstract= kn-abstract=This study aims to evaluate the output factors (OPF) of different radiation therapy planning systems (TPSs) using a plastic scintillator detector (PSD). The validation results for determining a practical field size for clinical use were verified. The implemented validation system was an Exradin W2 PSD. The focus was to validate the OPFs of the small irradiation fields of two modeled radiation TPSs using RayStation version 10.0.1 and Monaco version 5.51.10. The linear accelerator used for irradiation was a TrueBeam with three energies: 4, 6, and 10 MV. RayStation calculations showed that when the irradiation field size was reduced from 10?~?10 to 0.5?~?0.5 cm2, the results were within 2.0% of the measured values for all energies. Similarly, the values calculated using Monaco were within approximately 2.0% of the measured values for irradiation field sizes between 10?~?10 and 1.5?~?1.5 cm2 for all beam energies of interest. Thus, PSDs are effective validation tools for OPF calculations in TPS. A TPS modeled with the same source data has different minimum irradiation field sizes that can be calculated. These findings could aid in verification of equipment accuracy for treatment planning requiring highly accurate dose calculations and for third-party evaluation of OPF calculations for TPS. en-copyright= kn-copyright= en-aut-name=AndoYasuharu en-aut-sei=Ando en-aut-mei=Yasuharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OkadaMasahiro en-aut-sei=Okada en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MatsumotoNatsuko en-aut-sei=Matsumoto en-aut-mei=Natsuko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=IkuhiroKawasaki en-aut-sei=Ikuhiro en-aut-mei=Kawasaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=IshiharaSoichiro en-aut-sei=Ishihara en-aut-mei=Soichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KiriuHiroshi en-aut-sei=Kiriu en-aut-mei=Hiroshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= affil-num=1 en-affil=Hiroshima City Hospital kn-affil= affil-num=2 en-affil=Hiroshima City North Medical Center Asa Citizens Hospital kn-affil= affil-num=3 en-affil=Hiroshima City North Medical Center Asa Citizens Hospital kn-affil= affil-num=4 en-affil=Hiroshima City North Medical Center Asa Citizens Hospital kn-affil= affil-num=5 en-affil=Hiroshima City Hospital kn-affil= affil-num=6 en-affil=Hiroshima City Hospital kn-affil= affil-num=7 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= en-keyword=Plastic scintillator kn-keyword=Plastic scintillator en-keyword=Radiation therapy kn-keyword=Radiation therapy en-keyword=Small irradiation field kn-keyword=Small irradiation field en-keyword=Output factor kn-keyword=Output factor END start-ver=1.4 cd-journal=joma no-vol=13 cd-vols= no-issue=6 article-no= start-page=1783 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240320 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Enhancing Diagnostic Precision: Evaluation of Preprocessing Filters in Simple Diffusion Kurtosis Imaging for Head and Neck Tumors en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background: Our initial clinical study using simple diffusion kurtosis imaging (SDI), which simultaneously produces a diffusion kurtosis image (DKI) and an apparent diffusion coefficient map, confirmed the usefulness of SDI for tumor diagnosis. However, the obtained DKI had noticeable variability in the mean kurtosis (MK) values, which is inherent to SDI. We aimed to improve this variability in SDI by preprocessing with three different filters (Gaussian [G], median [M], and nonlocal mean) of the diffusion-weighted images used for SDI. Methods: The usefulness of filter parameters for diagnosis was examined in basic and clinical studies involving 13 patients with head and neck tumors. Results: The filter parameters, which did not change the median MK value, but reduced the variability and significantly homogenized the MK values in tumor and normal tissues in both basic and clinical studies, were identified. In the receiver operating characteristic curve analysis for distinguishing tumors from normal tissues using MK values, the area under curve values significantly improved from 0.627 without filters to 0.641 with G (sigma = 0.5) and 0.638 with M (radius = 0.5). Conclusions: Thus, image pretreatment with G and M for SDI was shown to be useful for improving tumor diagnosis in clinical practice. en-copyright= kn-copyright= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshimuraYuuki en-aut-sei=Yoshimura en-aut-mei=Yuuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=4 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=11 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=12 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=13 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=15 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=16 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=diffusion-weighted image kn-keyword=diffusion-weighted image en-keyword=Gaussian filter kn-keyword=Gaussian filter en-keyword=head and neck tumor kn-keyword=head and neck tumor en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=median filter kn-keyword=median filter en-keyword=nonlocal mean filter kn-keyword=nonlocal mean filter en-keyword=phantom kn-keyword=phantom en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=restricted diffusion-weighted image kn-keyword=restricted diffusion-weighted image END start-ver=1.4 cd-journal=joma no-vol=47 cd-vols= no-issue=2 article-no= start-page=589 end-page=596 dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240219 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluation of the effect of sagging correction calibration errors in radiotherapy software on image matching en-subtitle= kn-subtitle= en-abstract= kn-abstract=To investigate the impact of sagging correction calibration errors in radiotherapy software on image matching. Three software applications were used, with and without a polymethyl methacrylate rod supporting the ball bearings (BB). The calibration error for sagging correction across nine flex maps (FMs) was determined by shifting the BB positions along the Left?Right (LR), Gun?Target (GT), and Up?Down (UD) directions from the reference point. Lucy and pelvic phantom cone-beam computed tomography (CBCT) images underwent auto-matching after modifying each FM. Image deformation was assessed in orthogonal CBCT planes, and the correlations among BB shift magnitude, deformation vector value, and differences in auto-matching were analyzed. The average difference in analysis results among the three softwares for the Winston?Lutz test was within 0.1 mm. The determination coefficients (R2) between the BB shift amount and Lucy phantom matching error in each FM were 0.99, 0.99, and 1.00 in the LR-, GT-, and UD-directions, respectively. The pelvis phantom demonstrated no cross-correlation in the GT direction during auto-matching error evaluation using each FM. The correlation coefficient (r) between the BB shift and the deformation vector value was 0.95 on average for all image planes. Slight differences were observed among software in the evaluation of the Winston?Lutz test. The sagging correction calibration error in the radiotherapy imaging system was caused by an auto-matching error of the phantom and deformation of CBCT images. en-copyright= kn-copyright= en-aut-name=YamazawaYumi en-aut-sei=Yamazawa en-aut-mei=Yumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OsakaAkitane en-aut-sei=Osaka en-aut-mei=Akitane kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=FujiiYasushi en-aut-sei=Fujii en-aut-mei=Yasushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=NakayamaTakahiro en-aut-sei=Nakayama en-aut-mei=Takahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=NishiokaKunio en-aut-sei=Nishioka en-aut-mei=Kunio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Department of Radiology, Niigata Prefectural Central Hospital kn-affil= affil-num=2 en-affil=Department of Radiology, Niigata Prefectural Central Hospital kn-affil= affil-num=3 en-affil=Department of Radiology, Chugoku Central Hospital of the Mutual Aid Association of Public School Teachers kn-affil= affil-num=4 en-affil=Department of Radiology, Chugoku Central Hospital of the Mutual Aid Association of Public School Teachers kn-affil= affil-num=5 en-affil=Department of Radiology, Tokuyama Central Hospital kn-affil= affil-num=6 en-affil=Faculty of Medicine, Graduate School of Health Sciences, Okayama University kn-affil= en-keyword=Radiotherapy kn-keyword=Radiotherapy en-keyword=Sagging correction kn-keyword=Sagging correction en-keyword=Image matching kn-keyword=Image matching en-keyword=Winston-Lutz test kn-keyword=Winston-Lutz test en-keyword=Deformable registration kn-keyword=Deformable registration END start-ver=1.4 cd-journal=joma no-vol=55 cd-vols= no-issue=1 article-no= start-page=4 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240102 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluating the index of panoramic X-ray image quality using K-means clustering method en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background A panoramic X-ray image is generally considered optimal when the occlusal plane is slightly arched, presenting with a gentle curve. However, the ideal angle of the occlusal plane has not been determined. This study provides a simple evaluation index for panoramic X-ray image quality, built using various image and cluster analyzes, which can be used as a training tool for radiological technologists and as a reference for image quality improvement.
Results A reference panoramic X-ray image was acquired using a phantom with the Frankfurt plane positioned horizontally, centered in the middle, and frontal plane centered on the canine teeth. Other images with positioning errors were acquired with anteroposterior shifts, vertical rotations of the Frankfurt plane, and horizontal left/right rotations. The reference and positioning-error images were evaluated with the cross-correlation coefficients for the occlusal plane profile, left/right angle difference, peak signal-to-noise ratio (PSNR), and deformation vector fields (DVF). The results of the image analyzes were scored for positioning-error images using K-means clustering analysis. Next, we analyzed the correlations between the total score, cross-correlation analysis of the occlusal plane curves, left/right angle difference, PSNR, and DVF. In the scoring, the positioning-error images with the highest quality were the ones with posterior shifts of 1 mm. In the analysis of the correlations between each pair of results, the strongest correlations (r?=?0.7?0.9) were between all combinations of PSNR, DVF, and total score.
Conclusions The scoring of positioning-error images using K-means clustering analysis is a valid evaluation indicator of correct patient positioning for technologists in training. en-copyright= kn-copyright= en-aut-name=ImajoSatoshi en-aut-sei=Imajo en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamuraNobue en-aut-sei=Nakamura en-aut-mei=Nobue kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HondaMitsugi en-aut-sei=Honda en-aut-mei=Mitsugi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil=Division of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=2 en-affil=Faculty of Medicine, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Division of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=4 en-affil=Division of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=5 en-affil=Faculty of Medicine, Graduate School of Health Sciences, Okayama University kn-affil= en-keyword=Quality improvement kn-keyword=Quality improvement en-keyword=Signal-to-noise ratio kn-keyword=Signal-to-noise ratio en-keyword=Panoramic X-ray images kn-keyword=Panoramic X-ray images en-keyword=Cluster analysis kn-keyword=Cluster analysis en-keyword=Occlusal plane kn-keyword=Occlusal plane END start-ver=1.4 cd-journal=joma no-vol=13 cd-vols= no-issue=24 article-no= start-page=3619 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20231207 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Characteristic Mean Kurtosis Values in Simple Diffusion Kurtosis Imaging of Dentigerous Cysts en-subtitle= kn-subtitle= en-abstract= kn-abstract=We evaluated the usefulness of simple diffusion kurtosis (SD) imaging, which was developed to generate diffusion kurtosis images simultaneously with an apparent diffusion coefficient (ADC) map for 27 cystic disease lesions in the head and neck region. The mean kurtosis (MK) and ADC values were calculated for the cystic space. The MK values were dentigerous cyst (DC): 0.74, odontogenic keratocyst (OKC): 0.86, ranula (R): 0.13, and mucous cyst (M): 0, and the ADC values were DC: 1364 ~ 10?6 mm2/s, OKC: 925 ~ 10?6 mm2/s, R: 2718 ~ 10?6 mm2/s, and M: 2686 ~ 10?6 mm2/s. The MK values of DC and OKC were significantly higher than those of R and M, whereas their ADC values were significantly lower. One reason for the characteristic signal values in diffusion-weighted images of DC may be related to content components such as fibrous tissue and exudate cells. When imaging cystic disease in the head and neck region using SD imaging, the maximum b-value setting at the time of imaging should be limited to approximately 1200 s/mm2 for accurate MK value calculation. This study is the first to show that the MK values of DC are characteristically higher than those of other cysts. en-copyright= kn-copyright= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=Al-HammadWlla en-aut-sei=Al-Hammad en-aut-mei=Wlla kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=KamaruddinNurul en-aut-sei=Kamaruddin en-aut-mei=Nurul kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=YanagiYoshinobu en-aut-sei=Yanagi en-aut-mei=Yoshinobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=18 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=7 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=9 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=10 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=11 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=12 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=13 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=14 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=15 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=16 en-affil=Department of Oral Rehabilitation and Regenerative Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=18 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=dentigerous cyst kn-keyword=dentigerous cyst en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=head and neck kn-keyword=head and neck en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging en-keyword=apparent diffusion coefficient value kn-keyword=apparent diffusion coefficient value en-keyword=diffusion kurtosis imaging kn-keyword=diffusion kurtosis imaging END start-ver=1.4 cd-journal=joma no-vol=26 cd-vols= no-issue=5 article-no= start-page=536 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20231002 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath?hold radiotherapy after breast cancer surgery en-subtitle= kn-subtitle= en-abstract= kn-abstract=Increased heart dose during postoperative radiotherapy (RT) for left?sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath?hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left?sided BC. However, treatment planning and DIBH for RT are laborious, time?consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre?select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right?left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve?receiver operating characteristic of 0.88, for a cut?off value of 300 cGy. The present study suggested that ML can be used to pre?select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC. en-copyright= kn-copyright= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=Al?HammadWlla en-aut-sei=Al?Hammad en-aut-mei=Wlla kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=IshizakaHinata en-aut-sei=Ishizaka en-aut-mei=Hinata kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=HiranoMasaki en-aut-sei=Hirano en-aut-mei=Masaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=MutoYuki en-aut-sei=Muto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=IharaHiroki en-aut-sei=Ihara en-aut-mei=Hiroki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=SugiyamaSoichi en-aut-sei=Sugiyama en-aut-mei=Soichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An?Najah National University kn-affil= affil-num=11 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=12 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=13 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=15 en-affil=Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=16 en-affil=Department of Proton Beam Therapy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=BC kn-keyword=BC en-keyword=RT kn-keyword=RT en-keyword=heart dose kn-keyword=heart dose en-keyword=ML kn-keyword=ML en-keyword=DNN kn-keyword=DNN en-keyword=DIBH kn-keyword=DIBH END start-ver=1.4 cd-journal=joma no-vol=30 cd-vols= no-issue=8 article-no= start-page=7412 end-page=7424 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230804 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients en-subtitle= kn-subtitle= en-abstract= kn-abstract=Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD & GE; 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH. en-copyright= kn-copyright= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=IshizakaHinata en-aut-sei=Ishizaka en-aut-mei=Hinata kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=11 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=12 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=13 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= en-keyword=breast cancer kn-keyword=breast cancer en-keyword=radiotherapy kn-keyword=radiotherapy en-keyword=heart dose kn-keyword=heart dose en-keyword=machine learning kn-keyword=machine learning en-keyword=deep neural network kn-keyword=deep neural network en-keyword=deep inspiration breath-hold technique kn-keyword=deep inspiration breath-hold technique en-keyword=computed tomography kn-keyword=computed tomography END start-ver=1.4 cd-journal=joma no-vol=29 cd-vols= no-issue=2 article-no= start-page=85 end-page=91 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230504 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Objective evaluation method using multiple image analyses for panoramic radiography improvement en-subtitle= kn-subtitle= en-abstract= kn-abstract=Introduction: In the standardization of panoramic radiography quality, the education and training of beginners on panoramic radiographic imaging are important. We evaluated the relationship between positioning error factors and multiple image analysis results for reproducible panoramic radiography.
Material and methods: Using a panoramic radiography system and a dental phantom, reference images were acquired on the Frankfurt plane along the horizontal direction, midsagittal plane along the left-right direction, and for the canine on the forward-backward plane. Images with positioning errors were acquired with 1-5 mm shifts along the forward-backward direction and 2-10 degrees rotations along the horizontal (chin tipped high/low) and vertical (left-right side tilt) directions on the Frankfurt plane. The cross-correlation coefficient and angle difference of the occlusion congruent plane profile between the reference and positioning error images, peak signal-to-noise ratio (PSNR), and deformation vector value by deformable image registration were compared and evaluated.
Results: The cross-correlation coefficients of the occlusal plane profiles showed the greatest change in the chin tipped high images and became negatively correlated from 6 degrees image rotation (r = -0.29). The angle difference tended to shift substantially with increasing positioning error, with an angle difference of 8.9 degrees for the 10 degrees chin tipped low image. The PSNR was above 30 dB only for images with a 1-mm backward shift. The positioning error owing to the vertical rotation was the largest for the deformation vector value.
Conclusions: Multiple image analyses allow to determine factors contributing to positioning errors in panoramic radiography and may enable error correction. This study based on phantom imaging can support the education of beginners regarding panoramic radiography. en-copyright= kn-copyright= en-aut-name=ImajoSatoshi en-aut-sei=Imajo en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamuraNobue en-aut-sei=Nakamura en-aut-mei=Nobue kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HondaMitsugi en-aut-sei=Honda en-aut-mei=Mitsugi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Division of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=4 en-affil=Division of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University kn-affil= en-keyword=panoramic radiography kn-keyword=panoramic radiography en-keyword=quantitative evaluation kn-keyword=quantitative evaluation en-keyword=deformable image registration kn-keyword=deformable image registration en-keyword=peak signal-to-noise ratio kn-keyword=peak signal-to-noise ratio END start-ver=1.4 cd-journal=joma no-vol=77 cd-vols= no-issue=3 article-no= start-page=273 end-page=280 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=202306 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Usefulness of Simple Diffusion Kurtosis Imaging for Head and Neck Tumors: An Early Clinical Study en-subtitle= kn-subtitle= en-abstract= kn-abstract=Diffusion kurtosis (DK) imaging (DKI), a type of restricted diffusion-weighted imaging, has been reported to be useful for tumor diagnoses in clinical studies. We developed a software program to simultaneously create DK images with apparent diffusion coefficient (ADC) maps and conducted an initial clinical study. Multi-shot echo-planar diffusion-weighted images were obtained at b-values of 0, 400, and 800 sec/mm2 for simple DKI, and DK images were created simultaneously with the ADC map. The usefulness of the DK image and ADC map was evaluated using a pixel analysis of all pixels and a median analysis of the pixels of each case. Tumor and normal tissues differed significantly in both pixel and median analyses. In the pixel analysis, the area under the curve was 0.64 for the mean kurtosis (MK) value and 0.77 for the ADC value. In the median analysis, the MK value was 0.74, and the ADC value was 0.75. The MK and ADC values correlated moderately in the pixel analysis and strongly in the median analysis. Our simple DKI system created DK images simultaneously with ADC maps, and the obtained MK and ADC values were useful for differentiating head and neck tumors from normal tissue. en-copyright= kn-copyright= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=ImajohSatoshi en-aut-sei=Imajoh en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=BamgboseBabatunde O. en-aut-sei=Bamgbose en-aut-mei=Babatunde O. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=YanagiYoshinobu en-aut-sei=Yanagi en-aut-mei=Yoshinobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=7 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=8 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=11 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=12 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=13 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=15 en-affil=Department of Oral Diagnostic Sciences, Faculty of Dentistry, Bayero University kn-affil= affil-num=16 en-affil=Department of Dental Informatics, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=clinical trial kn-keyword=clinical trial en-keyword=head and neck tumor kn-keyword=head and neck tumor en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging END start-ver=1.4 cd-journal=joma no-vol=25 cd-vols= no-issue=3 article-no= start-page=109 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=2023124 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Quantitative evaluation of the reduction of distortion and metallic artifacts in magnetic resonance images using the multiacquisition variable?resonance image combination selective sequence en-subtitle= kn-subtitle= en-abstract= kn-abstract=Magnetic resonance imaging (MRI) is superior to computed tomography (CT) in determining changes in tissue structure, such as those observed following inflammation and infection. However, when metal implants or other metal objects are present, MRI exhibits more distortion and artifacts compared with CT, which hinders the accurate measurement of the implants. A limited number of reports have examined whether the novel MRI sequence, multiacquisition variable-resonance image combination selective (MAVRIC SL), can accurately measure metal implants without distortion. Therefore, the present study aimed to demonstrate whether MAVRIC SL could accurately measure metal implants without distortion and whether the area around the metal implants could be well delineated without artifacts. An agar phantom containing a titanium alloy lumbar implant was used for the present study and was imaged using a 3.0 T MRI machine. A total of three imaging sequences, namely MAVRIC SL, CUBE and magnetic image compilation (MAGiC), were applied and the results were compared. Distortion was evaluated by measuring the screw diameter and distance between the screws multiple times in the phase and frequency directions by two different investigators. The artifact region around the implant was examined using a quantitative method following standardization of the phantom signal values. It was revealed that MAVRIC SL was a superior sequence compared with CUBE and MAGiC, as there was significantly less distortion, a lack of bias between the two different investigators and significantly reduced artifact regions. These results suggested the possibility of utilizing MAVRIC SL for follow-up to observe metal implant insertions. en-copyright= kn-copyright= en-aut-name=HiranoMasaki en-aut-sei=Hirano en-aut-mei=Masaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=MutoYuki en-aut-sei=Muto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FujiwaraYuta en-aut-sei=Fujiwara en-aut-mei=Yuta kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=SasakiTomoaki en-aut-sei=Sasaki en-aut-mei=Tomoaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=ImajohSatoshi en-aut-sei=Imajoh en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=E. Al-HammadWlla en-aut-sei=E. Al-Hammad en-aut-mei=Wlla kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=O. BamgboseBabatunde en-aut-sei=O. Bamgbose en-aut-mei=Babatunde kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Division of Clinical Radiology Service, Okayama Central Hospital kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=11 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700?8558, Japan kn-affil= affil-num=12 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=13 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama, 770?8558, Japan kn-affil= affil-num=14 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=15 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=16 en-affil=Department of Oral Diagnostic Sciences, Faculty of Dentistry, Bayero University kn-affil= en-keyword=MAVRIC SL kn-keyword=MAVRIC SL en-keyword=metal artifacts kn-keyword=metal artifacts en-keyword=implant kn-keyword=implant en-keyword=phantom kn-keyword=phantom en-keyword=MRI kn-keyword=MRI END start-ver=1.4 cd-journal=joma no-vol=25 cd-vols= no-issue= article-no= start-page=100405 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=202301 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Patient-specific respiratory motion management using lung tumors vs fiducial markers for real-time tumor-tracking stereotactic body radiotherapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background and purpose: In real-time lung tumor-tracking stereotactic body radiotherapy (SBRT), tracking accuracy is related to radiotherapy efficacy. This study aimed to evaluate the respiratory movement relationship between a lung tumor and a fiducial marker position in each direction using four-dimensional (4D) computed tomography (CT) images.
Materials and methods: A series of 31 patients with a fiducial marker for lung SBRT was retrospectively analyzed using 4DCT. In the upper (UG) and middle and lower lobe groups (MLG), the cross-correlation coefficients of respiratory movement between the lung tumor and fiducial marker position in four directions (anterior?posterior, left?right, superior?inferior [SI], and three-dimensional [3D]) were calculated for each gating window (?1, ?2, and ? 3 mm). Subsequently, the proportions of phase numbers in unplanned irradiation (with lung tumors outside the gating window and fiducial markers inside the gating window) were calculated for each gating window.
Results: In the SI and 3D directions, the cross-correlation coefficients were significantly different between UG (mean r = 0.59, 0.63, respectively) and MLG (mean r = 0.95, 0.97, respectively). In both the groups, the proportions of phase numbers in unplanned irradiation were 11 %, 28 %, and 63 % for the ? 1-, ?2-, and ? 3-mm gating windows, respectively.
Conclusions: Compared with MLG, fiducial markers for UG have low cross-correlation coefficients between the lung tumor and the fiducial marker position. Using 4DCT to assess the risk of unplanned irradiation in a gating window setting and selecting a high cross-correlation coefficient fiducial marker in advance are important for accurate treatment using lung SBRT. en-copyright= kn-copyright= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KiritaniMichiru en-aut-sei=Kiritani en-aut-mei=Michiru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=DeguchiTomomi en-aut-sei=Deguchi en-aut-mei=Tomomi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HiraNanami en-aut-sei=Hira en-aut-mei=Nanami kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=TomimotoSyouta en-aut-sei=Tomimoto en-aut-mei=Syouta kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil=Faculty of Medicine, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Facilty of Health Sciences, Okayama University Medical School kn-affil= affil-num=3 en-affil=Facilty of Health Sciences, Okayama University Medical School kn-affil= affil-num=4 en-affil=Facilty of Health Sciences, Okayama University Medical School kn-affil= affil-num=5 en-affil=Facilty of Health Sciences, Okayama University Medical School kn-affil= en-keyword=Patient-specific respiratory motion management kn-keyword=Patient-specific respiratory motion management en-keyword=Stereotactic body radiotherapy kn-keyword=Stereotactic body radiotherapy en-keyword=Four-dimensional computed tomography kn-keyword=Four-dimensional computed tomography en-keyword=Fiducial marker kn-keyword=Fiducial marker en-keyword=Lung cancer kn-keyword=Lung cancer en-keyword=Gating window kn-keyword=Gating window END start-ver=1.4 cd-journal=joma no-vol=24 cd-vols= no-issue= article-no= start-page=82 end-page=87 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20221012 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Statistical evaluation of the effectiveness of dual amplitude-gated stereotactic body radiotherapy using fiducial markers and lung volume en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background and purpose: The low tracking accuracy of lung stereotactic body radiotherapy (SBRT) risks reduced treatment efficacy. We used four-dimensional computed tomography (4DCT) images to determine the correlation between changes in fiducial marker positions and lung volume for lung tumors, and we evaluated the effectiveness of the combined use of these images in lung SBRT.
Materials and methods: Data of 30 patients who underwent fiducial marker placement were retrospectively analyzed. We calculated the motion amplitudes of the center of gravity coordinates of the lung tumor and fiducial markers in each phase and the ipsilateral, contralateral, and bilateral lung volumes using 4DCT. Moreover, we calculated the cross-correlation coefficient between the fiducial marker position and the lung volume changes waveform for the motion amplitude waveform of the lung tumor over three gating windows (all phases, ?2 mm3, and ?3 mm3).
Results: Compared with the lung volume, approximately 30 % of the fiducial markers demonstrated a low correlation with the lung tumor. In the ?2 mm3 and ?3 mm3 gating windows, the cross-correlation coefficients between the lung tumor and the optimal marker (r > 0.9: 83 % and 86 %) were significantly different for all fiducial markers (r > 0.9: 39 %, 53 %) and the ipsilateral (r > 0.9: 35 % and 40 %), contralateral (r > 0.9: 44 % and 41 %), and bilateral (r > 0.9: 39 % and 45 %) lung volumes.
Conclusions: Some of the fiducial markers showed a low correlation with the lung tumor. This study indicated that the combined use of lung volume monitoring can improve tracking accuracy. en-copyright= kn-copyright= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=TanakaHidekazu en-aut-sei=Tanaka en-aut-mei=Hidekazu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiation Oncology, Yamaguchi University Graduate School of Medicine kn-affil= en-keyword=Fiducial marker kn-keyword=Fiducial marker en-keyword=Respiratory gating method kn-keyword=Respiratory gating method en-keyword=Stereotactic body radiotherapy kn-keyword=Stereotactic body radiotherapy en-keyword=Tumor tracking kn-keyword=Tumor tracking en-keyword=Lung cancer kn-keyword=Lung cancer en-keyword=4DCT kn-keyword=4DCT END start-ver=1.4 cd-journal=joma no-vol=47 cd-vols= no-issue=4 article-no= start-page=329 end-page=333 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20221031 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Statistical analysis of correlation of gamma passing results for two quality assurance phantoms used for patient-specific quality assurance in volumetric modulated arc radiotherapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=Patient-specific quality assurance (QA) data must be migrated from outdated QA systems to new ones to produce objective results that can be understood by oncologists. We aimed to evaluate a method for obtaining a high correlation of dose distributions according to various gamma passing rates among two types of 2D detectors for the migration of patient-specific QA data of volumetric modulated arc therapy (VMAT). The patient-specific QA of 20 patients undergoing VMAT was measured in two different modes: standard single measurement (SM) mode and multiple merged measurements (MM) techniques using Ar-cCHECK (AC) and OCTAVIUS (OT). The correlation of the measured and calculated dose distributions was evaluated according to varying gamma passing rates (3%/3 mm, 2%/3 mm, 2%/2 mm, and 1%/1 mm). The gamma passing rates were analyzed using the Anderson-Darling normality test. Treatment plan dose dis-tributions were calculated by intentionally shifting the calculation isocenter position (x,y,z +/- 0.5, +/- 1.0, +/- 1.5, and +/- 2.0 mm). The highest correlation between the SM and MM was observed with a gamma passing rate of 1%/1 mm with AC (r = 0.866) and 3%/2 mm with OT (r = 0.916). However, SM and MM did not follow a normal distribution with a rate of 3%/2 mm in OT. The second-highest correlation was obtained with a rate of 2%/2 mm (r = 0.900). Among the two 2D detectors, the highest correlation be-tween the calculated and measured dose distributions was obtained for a gamma passing rate of 1%/1 mm using SM in AC and 2%/2 mm using MM in OT (r = 0.716). Adjusting the gamma passing rate and measurement mode of AC and OT resulted in higher correlations between measured and calculated dose distributions. The high correlation between different 2D detectors objectively indicated a potential mi-gration method. This enabled the sharing of more accurate patient-specific QA data from 2D detectors with different phantoms. A high correlation was observed between the two types of detectors in this study (r = 0.716); therefore, the proposed method should be useful for oncologists to share information regarding patient-specific QA for VMAT. en-copyright= kn-copyright= en-aut-name=KuniiYuki en-aut-sei=Kunii en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamotoAkira en-aut-sei=Nakamoto en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=NishiokaKunio en-aut-sei=Nishioka en-aut-mei=Kunio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil=Department of Radiology, Tokuyama Central Hospital kn-affil= affil-num=2 en-affil=Faculty of Medicine, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiology, Tokuyama Central Hospital kn-affil= affil-num=4 en-affil=Department of Radiology, Tokuyama Central Hospital kn-affil= en-keyword=Volumetric modulated arc therapy (VMAT) kn-keyword=Volumetric modulated arc therapy (VMAT) en-keyword=Patient-specific quality assurance (QA) kn-keyword=Patient-specific quality assurance (QA) en-keyword=2D detector kn-keyword=2D detector en-keyword=Gamma passing rate kn-keyword=Gamma passing rate END start-ver=1.4 cd-journal=joma no-vol=47 cd-vols= no-issue=2 article-no= start-page=E13 end-page=E18 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20220103 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluation of patient-specific motion management for radiotherapy planning computed tomography using a statistical method en-subtitle= kn-subtitle= en-abstract= kn-abstract=We evaluated the probabilistic randomness of predictions by using individual numerical data based on general data for treatment planning computed tomography (CT) and evaluated the importance of patient-specific management through statistical analysis of our facility's data in lung stereotactic body radiotherapy (SBRT) and prostate volumetric modulated arc therapy (VMAT). The subjects were 30 patients who underwent lung SBRT with fiducial markers and 24 patients who underwent prostate VMAT. The average 3-dimensional (3D) displacement error between the fiducial marker and lung mass in 4DCT of lung SBRT was calculated and then compared with the 3D displacement error between the upper-lobe group (UG) and middle- or lower-lobe group (LG). The duty cycles between the lung tumor and fiducial marker at the <2-mm3 ambush area were compared between the UG and LG. In the prostate VMAT, the Shewhart control chart was analyzed by comparing multiple acquisition planning CT (MPCT) and cone-beam CT (CBCT) during the treatment period. The average 3D displacement errors in 4DCT for the lung tumor and fiducial marker were significantly different between the UG and middle- or lower-lobe group, but there was no correlation with the duty cycle. The Shewhart control chart for 3D displacement errors of the prostate for MPCT and CBCT showed that errors of >8 mm exceeded the control limit. In lung SBRT and prostate VMAT, overall statistical data from planning CT showed probabilistic randomness in predictions during the treatment period, and patient-specific motion management was needed to increase accuracy. A radiotherapy planning CT report showing a statistical analysis graph would be useful to objective share with staff. en-copyright= kn-copyright= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=EtoHidetoshi en-aut-sei=Eto en-aut-mei=Hidetoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiology, Yamaguchi University Hospital kn-affil= END