CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma.

Autor: Wakiya T; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan. wakiya1979@hirosaki-u.ac.jp., Ishido K; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan., Kimura N; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan., Nagase H; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan., Kanda T; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan., Ichiyama S; Hirosaki University School of Medicine, Hirosaki City, Aomori, 036-8562, Japan., Soma K; Hirosaki University School of Medicine, Hirosaki City, Aomori, 036-8562, Japan., Matsuzaka M; Department of Medical Informatics, Hirosaki University Hospital, Hirosaki City, Aomori, 036-8562, Japan., Sasaki Y; Department of Medical Informatics, Hirosaki University Hospital, Hirosaki City, Aomori, 036-8562, Japan., Kubota S; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan., Fujita H; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan., Sawano T; Department of Surgery, Aomori Prefectural Central Hospital, Aomori City, Aomori, 030-8553, Japan., Umehara Y; Department of Surgery, Aomori Prefectural Central Hospital, Aomori City, Aomori, 030-8553, Japan., Wakasa Y; Department of Surgery, Aomori City Hospital, Aomori City, Aomori, 0300821, Japan., Toyoki Y; Department of Surgery, Aomori City Hospital, Aomori City, Aomori, 0300821, Japan., Hakamada K; Department of Gastroenterological Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki City, Aomori, 036-8562, Japan.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 May 19; Vol. 12 (1), pp. 8428. Date of Electronic Publication: 2022 May 19.
DOI: 10.1038/s41598-022-12604-8
Abstrakt: Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence through the use of preoperative images. We collected the dataset, including preoperative plain computed tomography (CT) images, from 41 patients undergoing curative surgery for iCCA at multiple institutions. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The prediction accuracy of the model was analyzed. We defined early recurrence as recurrence within a year after surgical resection. Of the 41 patients, early recurrence was observed in 20 (48.8%). A total of 71,081 patches were extracted from the entire segmented tumor area of each patient. The average accuracy of the ResNet model for predicting early recurrence was 98.2% for the training dataset. In the validation dataset, the average sensitivity, specificity, and accuracy were 97.8%, 94.0%, and 96.5%, respectively. Furthermore, the area under the receiver operating characteristic curve was 0.994. Our CT-based DL model exhibited high predictive performance in projecting postoperative early recurrence, proposing a novel insight into iCCA management.
(© 2022. The Author(s).)
Databáze: MEDLINE
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