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Given the low sensitivity of current diagnostic methods, this study aimed to develop and evaluate deep learning models based on preoperative contrast-enhanced computed tomography images to improve the detection of occult peritoneal metastasis in T3\/T4 stage gastric cancer. We first evaluated the performance of several convolutional neural network architectures and identified Inception-ResNetV2 as the best-performing model. To further optimize the model\u2019s performance, we integrated multiple attention mechanism modules, with the SE module showing the most significant improvement. The SE-augmented Inception-ResNetV2 model achieved a receiver operating characteristic area under the curve of 0.973, Precision-Recall area under the curve of 0.908, and an F1-Score of 0.818, outperforming all other models. Calibration curves demonstrated good agreement between predicted and actual outcomes, while decision curve analysis highlighted the model\u2019s clinical utility. These findings suggest a potential approach for improving clinical predictive modeling by integrating advanced deep learning architectures with attention mechanisms. For patients identified as high-risk, further staging laparoscopy is recommended to minimize unnecessary surgery and guide treatment decisions.<\/jats:p>","DOI":"10.1371\/journal.pone.0349614","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:44:13Z","timestamp":1778780653000},"page":"e0349614","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of artificial intelligence based on contrast-enhanced CT imaging for predicting peritoneal metastasis in patients with T3\/T4 stage gastric cancer"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1856-4918","authenticated-orcid":true,"given":"Chao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daolai","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shizhuang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaodong","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianghua","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"pone.0349614.ref001","first-page":"209","article-title":"Global 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