Enterprise AI Analysis
Web-based explainable machine-learning tool for predicting five-year recurrence of colorectal cancer after curative resection: multicentre retrospective cohort study
The World Health Organization identifies colorectal cancer as the third-most diagnosed malignancy and second leading cause of cancer-related mortality worldwide. Up to 30% of patients relapse within 5 years postoperatively; however, conventional staging methods cannot reliably stratify individual risks, underscoring the need for precise, patient-centred decision-support tools. This study introduces an AI-CDSS, a web-based explainable machine-learning tool designed to predict five-year recurrence of colorectal cancer following curative resection. Utilizing data from 1,789 patients, four tree-based ML algorithms were trained on demographic, tumor, immunohistochemical, and laboratory features. The random forest model, chosen for its balanced performance, achieved 87% accuracy, 85% PPV, 87% NPV, and an F1 score of 0.64 on an independent validation set. Key predictors included pathological stage, M category, serum CEA/CA19-9, EGFR expression, targeted therapy, serum albumin, and inflammatory markers, collectively accounting for 43% of model importance. The AI-CDSS delivers personalized, real-time risk estimates within 1 second via an intuitive web interface, facilitating evidence-based, patient-centered treatment decisions grounded in local population data.
Executive Impact: Key Metrics
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Deep Analysis & Enterprise Applications
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Objective
Methods
Results
Conclusion
The objective of this study was to develop a precise, patient-centred decision-support tool to predict five-year recurrence of colorectal cancer after curative resection, addressing the limitations of conventional staging methods.
Researchers retrospectively analyzed data from 1,789 colorectal cancer patients, training four tree-based machine learning algorithms on demographic, tumour, immunohistochemical, and laboratory features. Patients were chronologically divided into training and validation sets, and feature importance was assessed using random forest impurity scores and Shapley additive explanations (SHAP).
Of the 1,789 patients, 406 (22.7%) experienced recurrence. The random forest-based system achieved 87% accuracy, 85% positive predictive value, 87% negative predictive value, and an F1 score of 0.64, with AUC values of 0.83-0.84 across all models. Key drivers of recurrence risk included tumour burden, biological markers, treatment intensity, and host factors.
The AI-CDSS provides personalized five-year recurrence-risk estimates for colorectal cancer patients within 1 second via an intuitive web interface, facilitating evidence-based, patient-centred treatment decisions grounded in local population data.
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Overall Prediction Accuracy
Enterprise Process Flow
Retrospective Data Analysis (1,789 Patients)
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Chronological Data Split (Training & Validation)
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Tree-Based ML Algorithm Training
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Feature Importance Assessment (Random Forest & SHAP)
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AI-CDSS Web-Based Deployment
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Impact on Clinical Workflow
A multi-centre retrospective cohort study demonstrated the system's ability to integrate diverse data points—demographic, tumour, immunohistochemical, and laboratory features—to offer a comprehensive risk assessment. The intuitive web interface allows clinicians to quickly input patient data and receive a personalized five-year recurrence probability within 1 second, significantly streamlining preoperative counselling and adjuvant therapy planning. This not only enhances diagnostic precision but also empowers shared decision-making, leading to more tailored and effective patient management strategies.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
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Phase 1: System Integration & Data Sync
Integrate AI-CDSS with existing hospital EHR systems and establish secure, real-time data synchronization protocols. Conduct initial data mapping and validation.
Phase 2: Clinician Training & Pilot Deployment
Conduct hands-on training for oncology teams, surgeons, and pathologists. Deploy AI-CDSS in a pilot program within selected departments for initial user feedback and workflow optimization.
Phase 3: Performance Monitoring & Iterative Refinement
Continuously monitor model performance against real-world patient outcomes. Gather qualitative feedback from clinicians to inform iterative refinements to the UI, features, and model parameters.
Phase 4: Full-Scale Rollout & Continuous Improvement
Expand AI-CDSS deployment across all relevant clinical sites. Establish a long-term strategy for regular model retraining, updates with new research, and integration of advanced features like genomic data.
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