Colorectal Cancer (CRC) is the 3rd most common cancer worldwide 🌍 , accounting for approximately 10% of all cancer cases.

According to world health organisation (WHO) - in 2022, an estimated 1.9 million new cases of colorectal cancer and more than 900 000 deaths 💀 occurred worldwide.

With the wave of AI-powered innovations transforming how we predict patient outcomes, stratify risk, and personalise treatment: Here are THREE landmark studies from 2025-2026, targeting colorectal cancer.

🔬 SurvFinder: Deep Learning Discovers Novel Prognostic Biomarkers

The Innovation: Researchers built a multi-view deep learning system that analyzes routine H&E slides from multiple angles to identify something pathologists rarely assess systematically: tertiary lymphoid structures (TLSs). These organised immune cell clusters form in response to tumours, and their location and maturity strongly predict outcomes.

The Achievement:

• 6,950 slides from 1,604 patients across 4 independent cohorts

• AUROC of 0.827 for predicting recurrence risk

• High-risk patients showed 8.23× higher hazard for relapse than low-risk

Why It Matters: SurvFinder discovered a clinically actionable biomarker hidden in ordinary microscope slides of tumour tissue. In a disease where 20% relapse post-surgery, this system allows for better treatment plans.

🩸 ColoLDB: Making Precision Medicine Accessible

The Innovation: This is a machine learning model that uses only standard laboratory parameters — complete blood counts, liver function tests, inflammatory markers, and basic metabolic panels — to predict CRC risk and outcomes.

The Achievement:

• Uses XGBoost and ensemble methods for robust predictions

• Requires no specialized equipment beyond existing lab infrastructure, which enables population-level screening in resource-limited settings

• Demonstrated comparable performance to more complex multi-modal systems in specific prediction tasks

Why It Matters: While digital pathology AI grabs headlines, ColoLDB addresses global health equity. By leveraging existing lab infrastructure, it brings AI-powered risk prediction to low-resource settings where CRC mortality remains highest.

🧬 HIBRID: When Two Tests Become One Smart Prediction

The Innovation: First system to combine AI analysis of microscope slides with circulating tumour DNA (ctDNA) blood tests — fusing analysis on how the tumour looks like and whether tumour DNA remains in the body.

The Achievement:

• Tested on 1,023 stage II patients and Identified four distinct risk strata enabling nuanced treatment decisions

• Found patients with scary-looking tumours but clean blood tests still face 18% recurrence risk

• Found patients with benign-looking tumours but positive blood tests face 31% recurrence risk

Why It Matters: HIBRID establishes multi-modal AI as clinically viable, not experimental. It transforms binary treatment decisions into probabilistic, personalised medicine — sparing low-risk patients chemotherapy toxicity while ensuring high-risk patients receive intervention.