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Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer.

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机构： [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China, [2]State Key Laboratory of Biotherapy, Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China, [3]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China, 4 Institute of Advanced Research, Infervision, Beijing, China

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关键词： stomach neoplasms peritoneal neoplasms deep learning tomography x-ray computed neural networks computer

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We aimed to develop a deep convolutional neural network (DCNN) model based on computed tomography (CT) images for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC). A total of 544 patients with AGC were retrospectively enrolled. Seventy-nine patients were confirmed with OPM during surgery or laparoscopy. CT images collected during the initial visit were randomly split into a training cohort and a testing cohort for DCNN model development and performance evaluation, respectively. A conventional clinical model using multivariable logistic regression was also developed to estimate the pretest probability of OPM in patients with gastric cancer. The DCNN model showed an AUC of 0.900 (95% CI: 0.851-0.953), outperforming the conventional clinical model (AUC = 0.670, 95% CI: 0.615-0.739; p < 0.001). The proposed DCNN model demonstrated the diagnostic detection of occult PM, with a sensitivity of 81.0% and specificity of 87.5% using the cutoff value according to the Youden index. Our study shows that the proposed deep learning algorithm, developed with CT images, may be used as an effective tool to preoperatively diagnose OPM in AGC. Copyright © 2020 Huang, Liu, Chen, He, Yu, Liu, Wu, Hu and Song.

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出版当年[2020]版：

大类 | 3 区医学

小类 | 3 区肿瘤学

最新[2023]版：

大类 | 3 区医学

小类 | 3 区肿瘤学

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第一作者机构： [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China,

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