start-ver=1.4 cd-journal=joma no-vol=4 cd-vols= no-issue=1 article-no= start-page=4 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2021 dt-pub=20210128 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Bone microarchitectural analysis using ultra-high-resolution CT in tiger vertebra and human tibia en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background To reveal trends in bone microarchitectural parameters with increasing spatial resolution on ultra-high-resolution computed tomography (UHRCT) in vivo and to compare its performance with that of conventional-resolution CT (CRCT) and micro-CT ex vivo. Methods We retrospectively assessed 5 tiger vertebrae ex vivo and 16 human tibiae in vivo. Seven-pattern and four-pattern resolution imaging were performed on tiger vertebra using CRCT, UHRCT, and micro-CT, and on human tibiae using UHRCT. We measured six microarchitectural parameters: volumetric bone mineral density (vBMD), trabecular bone volume fraction (bone volume/total volume, BV/TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), trabecular separation (Tb.Sp), and connectivity density (ConnD). Comparisons between different imaging resolutions were performed using Tukey or Dunnett T3 test. Results The vBMD, BV/TV, Tb.N, and ConnD parameters showed an increasing trend, while Tb.Sp showed a decreasing trend both ex vivo and in vivo. Ex vivo, UHRCT at the two highest resolutions (1024- and 2048-matrix imaging with 0.25-mm slice thickness) and CRCT showed significant differences (p <= 0.047) in vBMD (51.4 mg/cm(3) and 63.5 mg/cm(3)versus 20.8 mg/cm(3)), BV/TV (26.5% and 29.5% versus 13.8 %), Tb.N (1.3 l/mm and 1.48 l/mm versus 0.47 l/mm), and ConnD (0.52 l/mm(3) and 0.74 l/mm(3)versus 0.02 l/mm(3), respectively). In vivo, the 512- and 1024-matrix imaging with 0.25-mm slice thickness showed significant differences in Tb.N (0.38 l/mm versus 0.67 l/mm, respectively) and ConnD (0.06 l/mm(3)versus 0.22 l/mm(3), respectively). Conclusions We observed characteristic trends in microarchitectural parameters and demonstrated the potential utility of applying UHRCT for microarchitectural analysis. en-copyright= kn-copyright= en-aut-name=InaiRyota en-aut-sei=Inai en-aut-mei=Ryota kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NakaharaRyuichi en-aut-sei=Nakahara en-aut-mei=Ryuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MorimitsuYusuke en-aut-sei=Morimitsu en-aut-mei=Yusuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=AkagiNoriaki en-aut-sei=Akagi en-aut-mei=Noriaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=MarukawaYouhei en-aut-sei=Marukawa en-aut-mei=Youhei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=MatsushitaToshi en-aut-sei=Matsushita en-aut-mei=Toshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=TanakaTakashi en-aut-sei=Tanaka en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=TadaAkihiro en-aut-sei=Tada en-aut-mei=Akihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=HirakiTakao en-aut-sei=Hiraki en-aut-mei=Takao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=NasuYoshihisa en-aut-sei=Nasu en-aut-mei=Yoshihisa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=NishidaKeiichiro en-aut-sei=Nishida en-aut-mei=Keiichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=OzakiToshifumi en-aut-sei=Ozaki en-aut-mei=Toshifumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=KanazawaSusumu en-aut-sei=Kanazawa en-aut-mei=Susumu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= affil-num=1 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=2 en-affil=Intelligent Orthopaedic System Development, Okayama University Medical School kn-affil= affil-num=3 en-affil=Devision of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=4 en-affil=Devision of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=5 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=6 en-affil=Devision of Radiology, Medical Support Department, Okayama University Hospital kn-affil= affil-num=7 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=8 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=9 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=10 en-affil=Medical materials for musculoskeletal reconstruction, Okayama University Medical School kn-affil= affil-num=11 en-affil=Orthopaedic Surgery, Okayama University Medical School kn-affil= affil-num=12 en-affil=Orthopaedic Surgery, Okayama University Medical School kn-affil= affil-num=13 en-affil=Department of Radiology, Okayama University Medical School kn-affil= en-keyword=Osteoporosis kn-keyword=Osteoporosis en-keyword=Bone density kn-keyword=Bone density en-keyword=Tomography (x-ray computed) kn-keyword=Tomography (x-ray computed) en-keyword=X-ray microtomography kn-keyword=X-ray microtomography END start-ver=1.4 cd-journal=joma no-vol=39 cd-vols= no-issue= article-no= start-page=619 end-page=632 dt-received= dt-revised= dt-accepted= dt-pub-year=2021 dt-pub=2021323 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Imaging evaluation of hereditary renal tumors: a pictorial review en-subtitle= kn-subtitle= en-abstract= kn-abstract= More than 10 hereditary renal tumor syndromes (HRTSs) and related germline mutations have been reported with HRTS-associated renal and extrarenal manifestations with benign and malignant tumors. Radiologists play an important role in detecting solitary or multiple renal masses with or without extrarenal findings on imaging and may raise the possibility of an inherited predisposition to renal cell carcinoma, providing direction for further screening, intervention and surveillance of the patients and their close family members before the development of potentially lethal renal and extrarenal tumors. Renal cell carcinomas (RCCs) associated with von Hippel?Lindau disease are typically slow growing while RCCs associated with HRTSs, such as hereditary leiomyomatosis and renal cell carcinoma syndrome, are highly aggressive. Therefore, radiologists need to be familiar with clinical and imaging findings of renal and extrarenal manifestations of HRTSs. This article reviews clinical and imaging findings for the evaluation of patients with well-established HRTSs from a radiologistfs perspective to facilitate the clinical decision-making process for patient management. en-copyright= kn-copyright= en-aut-name=TanakaTakashi en-aut-sei=Tanaka en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KawashimaAkira en-aut-sei=Kawashima en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MarukawaYohei en-aut-sei=Marukawa en-aut-mei=Yohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KitayamaTakahiro en-aut-sei=Kitayama en-aut-mei=Takahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=MasaokaYoshihisa en-aut-sei=Masaoka en-aut-mei=Yoshihisa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KojimaKatsuhide en-aut-sei=Kojima en-aut-mei=Katsuhide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=IguchiToshihiro en-aut-sei=Iguchi en-aut-mei=Toshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=HirakiTakao en-aut-sei=Hiraki en-aut-mei=Takao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=KanazawaSusumu en-aut-sei=Kanazawa en-aut-mei=Susumu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= affil-num=1 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=2 en-affil=Department of Radiology, Mayo Clinic kn-affil= affil-num=3 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=4 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=5 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=6 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=7 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=8 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=9 en-affil=Department of Radiology, Okayama University Hospital kn-affil= en-keyword=Hereditary renal tumor syndrome kn-keyword=Hereditary renal tumor syndrome en-keyword=Von Hippel?Lindau disease kn-keyword=Von Hippel?Lindau disease en-keyword=Birt?Hogg?Dub? syndrome kn-keyword=Birt?Hogg?Dub? syndrome en-keyword=Tuberous sclerosis complex kn-keyword=Tuberous sclerosis complex en-keyword=Hereditary leiomyomatosis and renal cell carcinoma syndrome kn-keyword=Hereditary leiomyomatosis and renal cell carcinoma syndrome END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=1 end-page=8 dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=20200108 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Differentiation of Small (? 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning en-subtitle= kn-subtitle= en-abstract= kn-abstract=OBJECTIVE.
This study evaluated the utility of a deep learning method for determining whether a small (? 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT.
MATERIALS AND METHODS.
This retrospective study included 1807 image sets from 168 pathologically diagnosed small (? 4 cm) solid renal masses with four CT phases (unenhanced, corticomedullary, nephrogenic, and excretory) in 159 patients between 2012 and 2016. Masses were classified as malignant (n = 136) or benign (n = 32). The dataset was randomly divided into five subsets: four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images). The Inception-v3 architecture convolutional neural network (CNN) model was used. The AUC for malignancy and accuracy at optimal cutoff values of output data were evaluated in six different CNN models. Multivariate logistic regression analysis was also performed.
RESULTS.
Malignant and benign lesions showed no significant difference of size. The AUC value of corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022). The highest accuracy (88%) was achieved in corticomedullary phase images. Multivariate analysis revealed that the CNN model of corticomedullary phase was a significant predictor for malignancy compared with other CNN models, age, sex, and lesion size.
CONCLUSION.
A deep learning method with a CNN allowed acceptable differentiation of small (? 4 cm) solid renal masses in dynamic CT images, especially in the corticomedullary image model. en-copyright= kn-copyright= en-aut-name=TanakaTakashi en-aut-sei=Tanaka en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HuangYong en-aut-sei=Huang en-aut-mei=Yong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MarukawaYohei en-aut-sei=Marukawa en-aut-mei=Yohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TsuboiYuka en-aut-sei=Tsuboi en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=MasaokaYoshihisa en-aut-sei=Masaoka en-aut-mei=Yoshihisa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KojimaKatsuhide en-aut-sei=Kojima en-aut-mei=Katsuhide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=IguchiToshihiro en-aut-sei=Iguchi en-aut-mei=Toshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=HirakiTakao en-aut-sei=Hiraki en-aut-mei=Takao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=GobaraHideo en-aut-sei=Gobara en-aut-mei=Hideo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=YanaiHiroyuki en-aut-sei=Yanai en-aut-mei=Hiroyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=NasuYasutomo en-aut-sei=Nasu en-aut-mei=Yasutomo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=KanazawaSusumu en-aut-sei=Kanazawa en-aut-mei=Susumu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= affil-num=1 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=2 en-affil=Department of Medical Informatics, Okayama University Hospital kn-affil= affil-num=3 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=4 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=5 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=6 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=7 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=8 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=9 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=10 en-affil=Department of Radiology, Okayama University Hospital kn-affil= affil-num=11 en-affil=Department of Urology, Okayama University Hospital kn-affil= affil-num=12 en-affil=Department of Radiology, Okayama University Hospital kn-affil= en-keyword=MDCT kn-keyword=MDCT en-keyword=artificial intelligence kn-keyword=artificial intelligence en-keyword=kidney kn-keyword=kidney en-keyword=neoplasms kn-keyword=neoplasms en-keyword=neural network models kn-keyword=neural network models END