准确预测胃癌（GC）腹膜复发在临床中至关重要。肿瘤微环境中胶原蛋白的改变影响癌细胞的迁移和治疗反应。在此，我们提出了基于多任务机器学习的肿瘤相关胶原蛋白特征 (TACS)，该特征由多光子成像衍生的定量胶原蛋白特征组成，可同时预测腹膜复发 (TACSPR) 和无病生存 (TACSDFS)。 在 713 个连续研究中我们开发并验证了一种多任务机器学习模型，用于同时预测腹膜复发 (TACSPR) 和无病生存 (TACSDFS)，其中训练队列中有 275 名患者，内部验证队列有 222 名患者，外部验证队列有 216 名患者。评估该模型预测腹膜复发和预后的准确性及其与辅助化疗的关系。在三个队列中，TACSPR和TACSDFS分别与腹膜复发和无病生存率独立相关（均P<0.001）。 TACSPR 在所有三个队列中均表现出良好的腹膜复发性能。此外，TACSDFS 还显示出纳入患者的无病生存率令人满意的准确性。对于II期和III期疾病，辅助化疗改善了低TACSPR和低TACSDFS、或高TACSPR和低TACSDFS、或低TACSPR和高TACSDFS患者的生存期，但对高TACSPR和高TACSDFS患者没有影响。机器学习模型可以准确预测胃癌的腹膜复发和生存，并可以区分可能从辅助化疗中受益的患者。© 2024。作者获得国际胃癌协会和日本胃癌协会的独家许可。

Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS).Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated.The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS.The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.© 2024. The Author(s) under exclusive licence to The International Gastric Cancer Association and The Japanese Gastric Cancer Association.