International journal of surgery (London, England) · 2025
AI-powered pathobiology transformers predict prognosis and targeted therapy benefits in patients with colorectal cancer ovarian metastases: a multicohort study.
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Zhang Yuanxin, Tan Delin, Zhang Jieru, Han Shuai, Deng Weihao, Wang Hui, Song Yanwei, Li Hao, Peng Jingqi, Wang Sheng, Qin Ge, Wang Huaiming
Аннотация
BACKGROUND: Individualized postoperative management of colorectal ovarian metastases demands precision medicine tools, yet current approaches lack consideration of prognostic heterogeneity and targeted therapy benefit guidance and suffer from high costs and long turnaround times of genetic testing.
METHODS: In this retrospective, prospective multicohort study, we developed and validated an interpretable transformer-based transfer learning model to predict patient prognosis, targeted therapy benefits and molecular mutations by integrating digital pathology with RNA data. The performance of the model was assessed with the AUC, accuracy, sensitivity, specificity, PPV and NPV.
RESULTS: The model accurately predicted peritoneal recurrence, with AUCs of 0.90, 0.74, and 0.83 across patient cohorts. It also achieved precise prognostic stratification for peritoneal recurrence-free survival in the training (HR = 107.22, 95% CI 24.18-475.52; p<0.001), external test (HR = 4.97, 95% CI 1.16-21.37; p = 0.03), and prospective test (HR = 10.53, 95% CI 2.02-54.94; p = 0.01) sets. The model revealed a significant association between prognostic stratification and tumor microenvironment heterogeneity (p < 0.05), thereby enhancing its biological interpretability. Further analysis revealed that only patients classified as high risk with BRAF/RAS mutations could benefit from the addition of targeted therapy to adjuvant chemotherapy (HR 0.38, 95% CI 0.18-0.79; p = 0.007). Moreover, the model predicted BRAF/RAS mutations with AUCs of 0.96/0.94 in the training set, maintaining cross-cohort generalizability with AUCs of 0.64-0.83.
CONCLUSIONS: This pathobiology-based deep learning model can robustly detect prognosis and mutation and identify targeted therapy beneficiaries, serving as a potential precision tool in clinical decision-making for the management of colorectal ovarian metastases.
METHODS: In this retrospective, prospective multicohort study, we developed and validated an interpretable transformer-based transfer learning model to predict patient prognosis, targeted therapy benefits and molecular mutations by integrating digital pathology with RNA data. The performance of the model was assessed with the AUC, accuracy, sensitivity, specificity, PPV and NPV.
RESULTS: The model accurately predicted peritoneal recurrence, with AUCs of 0.90, 0.74, and 0.83 across patient cohorts. It also achieved precise prognostic stratification for peritoneal recurrence-free survival in the training (HR = 107.22, 95% CI 24.18-475.52; p<0.001), external test (HR = 4.97, 95% CI 1.16-21.37; p = 0.03), and prospective test (HR = 10.53, 95% CI 2.02-54.94; p = 0.01) sets. The model revealed a significant association between prognostic stratification and tumor microenvironment heterogeneity (p < 0.05), thereby enhancing its biological interpretability. Further analysis revealed that only patients classified as high risk with BRAF/RAS mutations could benefit from the addition of targeted therapy to adjuvant chemotherapy (HR 0.38, 95% CI 0.18-0.79; p = 0.007). Moreover, the model predicted BRAF/RAS mutations with AUCs of 0.96/0.94 in the training set, maintaining cross-cohort generalizability with AUCs of 0.64-0.83.
CONCLUSIONS: This pathobiology-based deep learning model can robustly detect prognosis and mutation and identify targeted therapy beneficiaries, serving as a potential precision tool in clinical decision-making for the management of colorectal ovarian metastases.