ID | 57928 |
Author |
Huang, Yong
Department of Medical Informatics, Okayama University Hospital
Marukawa, Yohei
Department of Radiology, Okayama University Hospital
Tsuboi, Yuka
Department of Radiology, Okayama University Hospital
Masaoka, Yoshihisa
Department of Radiology, Okayama University Hospital
Kojima, Katsuhide
Department of Radiology, Okayama University Hospital
Iguchi, Toshihiro
Department of Radiology, Okayama University Hospital
Hiraki, Takao
Department of Radiology, Okayama University Hospital
Gobara, Hideo
Department of Radiology, Okayama University Hospital
Yanai, Hiroyuki
Department of Radiology, Okayama University Hospital
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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. |
Keywords | MDCT
artificial intelligence
kidney
neoplasms
neural network models
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Published Date | 2020-01-08
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Publication Title |
American journal of roentgenology
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Publisher | American Roentgen Ray Society
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Start Page | 1
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End Page | 8
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ISSN | 0361-803X
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NCID | AA00521224
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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File Version | author
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PubMed ID | |
DOI | |
Related Url | isVersionOf https://doi.org/10.2214/AJR.19.22074
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