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The AI model was generated with clinical, handcrafted radiomic, and deep learning features. Model diagnostic performance was measured by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, while predictive performance was measured by Kaplan\u2013Meier curves.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the test cohort (<jats:italic>n<\/jats:italic>\u00a0=\u00a0257), the AUC, sensitivity, and specificity of our AI model for diagnosing SRCC were 0.786 (95% CI: 0.721\u20130.845), 77.3%, and 69.2%, respectively. For the entire cohort, patients with AI\u2010predicted high risk had a significantly shorter median OS compared with those with low risk (median overall survival [OS], 38.8\u00a0vs. 64.2 months, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.009). Importantly, in pathologically confirmed advanced SRCC patients, AI\u2010predicted high\u2010risk status was indicative of a shorter overall survival (median overall survival [OS], 31.0 vs. 54.4 months, <jats:italic>p\u00a0<\/jats:italic>=\u00a00.036) and marked chemotherapy resistance, whereas AI\u2010predicted low\u2010risk status had substantial chemotherapy benefit (median OS [without vs. with chemotherapy], 26.0 vs. not reached, <jats:italic>p\u00a0<\/jats:italic>=\u00a00.013).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The CT\u2010based AI model demonstrated good performance for diagnosing SRCC, stratifying patient prognosis, and predicting chemotherapy responses. Advanced SRCC patients with AI\u2010predicted low\u2010risk status may benefit substantially from adjuvant chemotherapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15437","type":"journal-article","created":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T07:58:06Z","timestamp":1642233486000},"page":"1535-1546","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Deep learning\u2010based AI model for signet\u2010ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer"],"prefix":"10.1002","volume":"49","author":[{"given":"Cong","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence University of Chinese Academy of Sciences  Beijing China"},{"name":"CAS Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex Systems Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences  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