Clinical Scorecard: Multimodal Integration of Endoscopic and Radiomic Data for Predicting Survival Outcomes in Colorectal Cancer
At a Glance
Category
Detail
Condition
Colorectal cancer
Key Mechanisms
Fusion of endoscopic images and pelvic CT scans using a multimodal selective state space framework (HydraMamba) for lesion segmentation, detection, and survival prediction
Target Population
Patients undergoing preoperative assessment for colorectal cancer
Care Setting
Clinical imaging and oncology diagnostic settings involving endoscopy and CT imaging
Key Highlights
HydraMamba achieved state-of-the-art lesion segmentation and detection performance on both endoscopic and CT datasets (Endoscopy Dice 0.856, F1 0.918; CT Dice 0.812, F1 0.888).
The model delivered well-calibrated survival prediction on CT data with high concordance indices (Harrell’s C index 0.832, Uno’s C@1y 0.853) and calibration slope near 1.01.
Multimodal fusion of endoscopic and radiologic data improves diagnostic accuracy and prognostic modeling beyond single modality approaches.
Guideline-Based Recommendations
Diagnosis
Utilize combined endoscopic and CT imaging data for comprehensive lesion characterization in colorectal cancer.
Apply advanced AI models like HydraMamba to enhance lesion detection and segmentation accuracy.
Management
Incorporate multimodal imaging analysis to inform preoperative planning and risk stratification.
Leverage imaging-derived prognostic biomarkers alongside clinicopathological factors for treatment decision-making.
Monitoring & Follow-up
Use AI-driven imaging assessments to monitor lesion progression and treatment response longitudinally.
Risks
Recognize challenges in generalizing AI models across diverse patient populations and imaging devices.
Ensure large, diverse datasets and robust architectures to maintain model performance and reliability.
Patient & Prescribing Data
Patients with colorectal cancer undergoing imaging evaluation
Multimodal imaging AI models provide noninvasive prognostic information that may complement traditional clinical risk factors and guide personalized management.
Clinical Best Practices
Integrate endoscopic and CT imaging data using multimodal AI frameworks for improved lesion analysis.
Validate AI model performance on diverse datasets to ensure generalizability.
Combine imaging biomarkers with clinical and pathological data for comprehensive patient assessment.
Adopt calibrated survival prediction models to support prognostication and treatment planning.
