Preoperative CT deep learning of tumor and visceral adipose tissue combined with postoperative pathology predicts colorectal cancer recurrence: a multicenter study.

Abstract

Introduction: To develop and externally validate a preoperative CT imaging model integrating primary tumour and visceral adipose tissue (VAT) features for predicting recurrence in colorectal cancer (CRC).

Materials and methods: This retrospective multicentre study included 1,369 patients with CRC undergoing upfront curative-intent resection, comprising a training cohort (n = 705) and two external test cohorts (n = 425 and 239). A 3D convolutional neural network extracted tumour-centred volumetric features, while an attention-based multiple instance learning branch aggregated whole-abdominal VAT slices into patient-level representations. The two CT-derived scores constituted the preoperative imaging model. A clinicopathological component was combined with the imaging scores to construct the postoperative multimodal model; robustness to variable selection was evaluated using an all-candidate ridge-penalised Cox sensitivity model.

Results: Both tumour and VAT DL scores remained independently associated with recurrence-free survival after multivariable adjustment. In external test cohorts 1 and 2, the preoperative Tumor + VAT imaging model achieved C-indices of 0.734 and 0.772, whereas the postoperative multimodal model achieved C-indices of 0.772 and 0.810 and 2-year time-dependent AUCs of 0.757 and 0.836, respectively. Training-derived tertile cut-offs from the postoperative model consistently stratified patients into low-, intermediate- and high-risk groups across all cohorts (all log-rank P < 0.001). In the two external cohorts, the clinicopathological-only model achieved C-indices of 0.709 and 0.715, compared with 0.772 and 0.810 for the postoperative multimodal model.

Conclusion: The preoperative CT model non-invasively stratified recurrence risk using tumour and whole-abdominal VAT phenotypes, while postoperative clinicopathological features further improved prognostic performance.