一种用于预测伴有腹膜转移的结直肠癌细胞减灭术完整性的深度学习模型☆

A deep-learning model to predict the completeness of cytoreductive surgery in colorectal cancer with peritoneal metastasis☆.

作者信息

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

机构信息

Department of Colorectal Surgery and Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China.

College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; College of Computers, Central South University, Changsha, China.

出版信息

Eur J Surg Oncol. 2025 Jul;51(7):109760. doi: 10.1016/j.ejso.2025.109760. Epub 2025 Mar 10.

DOI:10.1016/j.ejso.2025.109760

PMID:40174333

Abstract

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.

摘要

背景

伴有腹膜转移（PM）的结直肠癌（CRC）预后较差。腹膜癌指数（PCI）用于评估PM的范围并选择减瘤手术（CRS）。然而，PCI评分在指导患者选择CRS方面并不准确。

目的

我们通过深度学习开发了一种新的解耦特征对齐与融合（DeAF）人工智能框架，以辅助PM患者的选择并预测CRS的手术完整性。

方法

招募了来自四家三级医院的186例伴有PM的CRC患者。在训练队列中，使用深度学习通过对比CT图像，采用西姆西亚姆算法训练DeAF模型，然后融合临床病理参数以提高性能。在内部验证队列和三个外部队列中评估了通过ROC得出的准确性、敏感性、特异性和AUC。

结果

在内部验证队列中，DeAF模型在预测CRS完整性方面表现出强大的准确性，AUC为0.9（95%CI：0.793-1.000）。该模型可以指导合适患者的选择，并预测CRS的潜在益处。在三个外部队列中验证了其在预测CRS完整性方面的高预测性能，AUC值分别为0.906（95%CI：0.812-1.000）、0.960（95%CI：0.885-1.000）和0.933（95%CI：0.791-1.000）。