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JOURNAL ARTICLE

Retrospective imaging studies of gastric cancer: Study protocol clinical trial (SPIRIT Compliant)

Zixing Huang, Dan Liu, Xinzu Chen, Pengxin Yu, Jiangfen Wu, Bin Song, Jiankun Hu, Bing Wu
Medicine (Baltimore) 2020, 99 (8): e19157
32080093

INTRODUCTION: Peritoneal metastasis (PM) is a frequent condition in patients presenting with gastric cancer, especially in younger patients with advanced tumor stages. Computer tomography (CT) is the most common noninvasive modality for preoperative staging in gastric cancer. However, the challenges of limited CT soft tissue contrast result in poor CT depiction of small peritoneal tumors. The sensitivity for detecting PM remains low. About 16% of PM are undetected. Deep learning belongs to the category of artificial intelligence and has demonstrated amazing results in medical image analyses. So far, there has been no deep learning study based on CT images for the diagnosis of PM in gastric cancer.

WE PROPOSED A HYPOTHESIS: CT images in the primary tumor region of gastric cancer had valuable information that could predict occult PM of gastric cancer, which could be extracted effectively through deep learning.

OBJECTIVE: To develop a deep learning model for accurate preoperative diagnosis of PM in gastric cancer.

METHOD: All patients with gastric cancer were retrospectively enrolled. All patients were initially diagnosed as PM negative by CT and later confirmed as positive through surgery or laparoscopy. The dataset was randomly split into training cohort (70% of all patients) and testing cohort (30% of all patients). To develop deep convolutional neural network (DCNN) models with high generalizability, 5-fold cross-validation and model ensemble were utilized. The area under the receiver operating characteristic curve, sensitivity and specificity were used to evaluate DCNN models on the testing cohort.

DISCUSSION: This study will help us know whether deep learning can improve the performance of CT in diagnosing PM in gastric cancer.

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