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