| Item type |
itemtype_ver1(1) |
| 公開日 |
2023-12-04 |
| タイトル |
|
|
タイトル |
Creation of synthetic contrast-enhanced computed tomography images using deep neural networks to screen for renal cell carcinoma |
|
言語 |
en |
| 著者 |
Sassa, Naoto
Kameya, Yoshitaka
Takahashi, Tomoichi
Matsukawa, Yoshihisa
Majima, Tsuyoshi
Tsuruta, Katsuhisa
Kobayashi, Ikuo
Kajikawa, Keishi
Kawanishi, Hideji
Kurosu, Haruka
Yamagiwa, Sho
Takahashi, Masaya
Hotta, Kazuhiro
Yamada, Keiichi
Yamamoto, Tokunori
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
|
|
言語 |
en |
|
権利情報Resource |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
deep neural network |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
artificial intelligence |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
deep learning |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
renal cell carcinoma |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
kidney cancer |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
In this study, we elucidate if synthetic contrast enhanced computed tomography images created from plain computed tomography images using deep neural networks could be used for screening, clinical diagnosis, and postoperative follow-up of small-diameter renal tumors. This retrospective, multicenter study included 155 patients (artificial intelligence training cohort [n = 99], validation cohort [n = 56]) who underwent surgery for small-diameter (≤40 mm) renal tumors, with the pathological diagnosis of renal cell carcinoma, during 2010–2020. We created a learned deep neural networks using pix2pix. We examined the quality of the synthetic enhanced computed tomography images created using this deep neural networks and compared them with real enhanced computed tomography images using the zero-mean normalized cross-correlation parameter. We assessed concordance rates between real and synthetic images and diagnoses according to 10 urologists by creating a receiver operating characteristic curve and calculating the area under the curve. The synthetic computed tomography images were highly concordant with the real computed tomography images, regardless of the existence or morphology of the renal tumor. Regarding the concordance rate, a greater area under the curve was obtained with synthetic computed tomography (area under the curve = 0.892) than with only computed tomography (area under the curve = 0.720; p < 0.001). In conclusions, this study is the first to use deep neural networks to create a high-quality synthetic computed tomography image that was highly concordant with a real computed tomography image. Our synthetic computed tomography images could be used for urological diagnoses and clinical screening. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Nagoya University Graduate School of Medicine, School of Medicine |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
departmental bulletin paper |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| ID登録 |
|
|
ID登録 |
10.18999/nagjms.85.4.713 |
|
ID登録タイプ |
JaLC |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
URI |
|
|
関連識別子 |
https://www.med.nagoya-u.ac.jp/medlib/nagoya_j_med_sci/854.html |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
0027-7622 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2186-3326 |
| 書誌情報 |
en : Nagoya Journal of Medical Science
巻 85,
号 4,
p. 713-724,
発行日 2023-11
|
06_Sassa.pdf (2 MB)
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