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 radiologistfs 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