AI-Based Prediction of Liver Metastasis in Colorectal Cancer (A Retrospective Study)

A Multicenter, Retrospective, Observational Study to Develop and Validate a Multimodal Deep Learning Model for Predicting Metachronous Liver Metastasis in Colorectal Cancer Patients After Curative Resection

This multicenter, retrospective study aims to develop and validate a multimodal deep learning model for predicting the risk of metachronous liver metastasis in patients with stage I-III colorectal cancer following curative resection. The model will integrate preoperative contrast-enhanced CT imaging, digitized histopathological whole-slide images, and standard clinical-pathological data.

The primary objective is to assess the model's discriminatory performance, measured by the area under the receiver operating characteristic curve (AUC), and to compare its predictive accuracy against traditional prognostic factors such as TNM staging and serum carcinoembryonic antigen levels. This research utilizes existing archival data; no direct patient contact or intervention is involved. The ultimate goal is to provide a robust, data-driven tool for improved risk stratification, which could potentially guide personalized surveillance strategies and adjuvant therapy decisions in the future.

Inclusion Criteria:

• Age 18-75 years, any gender.
• Histologically confirmed primary colon or rectal adenocarcinoma.
• Underwent curative radical resection (R0 resection) for colorectal cancer.
• Preoperative contrast-enhanced abdominal/pelvic CT scan performed within 1 month before surgery, with acceptable image quality.
• No evidence of distant metastasis (including synchronous liver metastasis) on preoperative or intraoperative exploration.

Exclusion Criteria:

• History of other malignant tumors.
• Previous history of liver surgery or liver transplantation.
• Missing clinical, imaging, or pathological data required for the study.
• Death within the perioperative period (within 30 days after surgery).
• Lack of regular follow-up information.