European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2025
A deep-learning model to predict the completeness of cytoreductive surgery in colorectal cancer with peritoneal metastasis☆.
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Lin Qingfeng, Chen Can, Li Kangshun, Cao Wuteng, Wang Renjie, Fichera Alessandro, Han Shuai, Zou Xiangjun, Li Tian, Zou Peiru, Wang Hui, Ye Zaisheng, Yuan Zixu, Chinese Peritoneal Tumor Collaborative Group (CPTCG)
Аннотация
BACKGROUND: Colorectal cancer (CRC) with peritoneal metastasis (PM) is associated with poor prognosis. The Peritoneal Cancer Index (PCI) is used to evaluate the extent of PM and to select Cytoreductive Surgery (CRS). However, PCI score is not accurate to guide patient's selection for CRS.
OBJECTIVE: We have developed a novel AI framework of decoupling feature alignment and fusion (DeAF) by deep learning to aid selection of PM patients and predict surgical completeness of CRS.
METHODS: 186 CRC patients with PM recruited from four tertiary hospitals were enrolled. In the training cohort, deep learning was used to train the DeAF model using Simsiam algorithms by contrast CT images and then fuse clinicopathological parameters to increase performance. The accuracy, sensitivity, specificity, and AUC by ROC were evaluated both in the internal validation cohort and three external cohorts.
RESULTS: The DeAF model demonstrated a robust accuracy to predict the completeness of CRS with AUC of 0.9 (95 % CI: 0.793-1.000) in internal validation cohort. The model can guide selection of suitable patients and predict potential benefits from CRS. The high predictive performance in predicting CRS completeness were validated in three external cohorts with AUC values of 0.906(95 % CI: 0.812-1.000), 0.960(95 % CI: 0.885-1.000), and 0.933 (95 % CI: 0.791-1.000), respectively.
CONCLUSION: The novel DeAF framework can aid surgeons to select suitable PM patients for CRS and predict the completeness of CRS. The model can change surgical decision-making and provide potential benefits for PM patients.
OBJECTIVE: We have developed a novel AI framework of decoupling feature alignment and fusion (DeAF) by deep learning to aid selection of PM patients and predict surgical completeness of CRS.
METHODS: 186 CRC patients with PM recruited from four tertiary hospitals were enrolled. In the training cohort, deep learning was used to train the DeAF model using Simsiam algorithms by contrast CT images and then fuse clinicopathological parameters to increase performance. The accuracy, sensitivity, specificity, and AUC by ROC were evaluated both in the internal validation cohort and three external cohorts.
RESULTS: The DeAF model demonstrated a robust accuracy to predict the completeness of CRS with AUC of 0.9 (95 % CI: 0.793-1.000) in internal validation cohort. The model can guide selection of suitable patients and predict potential benefits from CRS. The high predictive performance in predicting CRS completeness were validated in three external cohorts with AUC values of 0.906(95 % CI: 0.812-1.000), 0.960(95 % CI: 0.885-1.000), and 0.933 (95 % CI: 0.791-1.000), respectively.
CONCLUSION: The novel DeAF framework can aid surgeons to select suitable PM patients for CRS and predict the completeness of CRS. The model can change surgical decision-making and provide potential benefits for PM patients.