Prognostic value of deep-learning computational pathology in colorectal cancer: a systematic review of haematoxylin and eosin whole-slide image studies

Background Deep-learning computational pathology applied to routine haematoxylin and eosin whole-slide images may provide prognostic information in colorectal cancer. This systematic review evaluated the association between image-derived risk groups and survival. Methods PubMed and Web of Science Core Collection were searched from inception to 21 June 2026. Studies of colorectal cancer reporting a high-versus-low-risk hazard ratio derived from haematoxylin and eosin images were included. The most-adjusted estimate was selected, with preference for external validation. Random-effects meta-analysis, subgroup and leave-one-out analyses were performed. The recovered risk-of-bias panel reports Quality in Prognosis Studies domain judgements. The review was registered in PROSPERO (CRD420261429467). Results Across 27 studies, the pooled hazard ratio was 2.49 (95% confidence interval 2.12–2.93), with substantial heterogeneity (I² = 77%) and a 95% prediction interval of 1.25–4.97. Pooled estimates were 2.36 in externally validated studies (17 studies) and 2.74 in internally validated studies (10 studies); the difference was not statistically significant. Leave-one-out estimates ranged from 2.35 to 2.57. Conclusions Image-derived risk scores were associated with survival in colorectal cancer. Heterogeneity, differences in risk definitions and possible overlap between validation cohorts limit interpretation of a single pooled estimate. Independent prospective validation and evaluation of clinical utility are needed before these methods can guide treatment decisions.