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.<br/> 
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. <br/> MATERIALS AND METHODS.<br/> 
 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. <br/> 
RESULTS. <br/> 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. <br/> 
CONCLUSION. <br/> 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