AI and Cancer Research ── Colorectal ── 2026-11-07
Colorectal cancer: screening, treatment, trials and AI
- Abstract
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.
Journal IF: unknown (could not be matched)Reference: Xu Y, Zhang L, Wang C, Yao Y, Shen C, Gu W. Prognostic value of deep-learning computational pathology in colorectal cancer: a systematic review of haematoxylin and eosin whole-slide image studies. Research Square. 2026 Nov 4 [Epub ahead of print]. doi:10.21203/rs.3.rs-11297991/v1.Checked: Abstract only - Abstract
BACKGROUND: Severe postoperative complications after rectal cancer surgery increase morbidity, delay recovery, and may compromise oncologic outcomes. Existing risk models show limited accuracy and generalizability. Machine learning can integrate complex clinical and surgical variables to improve individualized risk prediction. OBJECTIVE: To develop and prospectively validate machine learning-based models for predicting severe postoperative complications after rectal cancer surgery. DESIGN: Retrospective model development and prospective temporal internal validation study. SETTING: Single tertiary referral center in South Korea. PATIENTS: A total of 3,503 patients who underwent curative rectal cancer resection with anastomosis between 2011 and 2025 were included, comprising 3,402 retrospective and 101 prospective cases. MAIN OUTCOME MEASURES: Severe postoperative complications defined as Clavien-Dindo grade ≥ 3 within 30days after surgery. RESULTS: Severe complications occurred in 9.8% of the retrospective and 13.9% of the prospective cohort. Four machine-learning algorithms - logistic regression, random forest, artificial neural network, and gradient boosting - were trained using 5-fold crossvalidation. In internal validation, the random forest model achieved the best performance (area under the receiver operating characteristic Curve 0.898; Sensitivity 0.685), while gradient boosting performed best in the temporal internal validation cohort (area under the receiver operating characteristic Curve 0.910; Sensitivity 0.707). The most influential predictors were tumor perforation, intraoperative transfusion, protective stoma, bowel obstruction, and cerebrovascular accident. LIMITATIONS: Validation was single institution (temporal internal, not external) with a small prospective cohort. CONCLUSIONS: Machine learning-based models accurately predicted severe postoperative complications after rectal cancer surgery, with strong discrimination maintained on temporal internal validation. Applied immediately after surgery, they may support individualized postoperative risk stratification and monitoring; external validation is warranted. See Video Abstract.
Journal IF-equivalent: 1.7 (OpenAlex 2-year mean citedness, value as of 2026-11-05, retrieved 2026-11-06; not the official Clarivate IF)Reference: Song SH, Kwon HK, Park JS, Park SY, Kim HJ, Lee J, et al. Machine Learning-Based Risk Scoring Using Clinical and Surgical Variables for Severe Complications after Rectal Cancer Surgery. Diseases of the Colon & Rectum. 2026 Nov 4 [Epub ahead of print]. doi:10.1097/dcr.0000000000004526.Checked: Abstract only