OncoSentinel™ — Clinical Relapse Risk Stratification
AI-powered cancer relapse prediction. Built for clinical validation and regulatory review.
OncoSentinel™ is Bracy Analytics' flagship predictive ML platform — designed to estimate cancer relapse probability, time-to-relapse, risk category, and key contributing risk factors for individual patients across cancer types, stages, and treatment histories.
Cancer relapse is one of the most consequential — and underserved — clinical prediction problems in oncology. Existing tools are fragmented, disease-specific, and rarely validated across diverse patient populations. OncoSentinel™ addresses this gap with a multi-algorithm ensemble approach that evaluates patient demographics, cancer characteristics, treatment history, clinical history, and biomarkers to produce four actionable outputs: relapse probability, estimated time-to-relapse, risk category (Low / Moderate / High), and the key contributing risk factors driving each prediction. The platform is being developed and validated against retrospective oncology datasets from academic institutions, public repositories, pharmaceutical clinical trials, and cancer registries — with a rigorous three-tier validation strategy designed to meet clinical and regulatory standards.
What We Deliver
Relapse Probability Estimation
Patient-level probability of cancer relapse at 1-year, 3-year, and 5-year intervals — derived from a multi-algorithm ensemble including Random Forest, XGBoost, survival analysis models, deep neural networks, and ensemble learning methods.
Time-to-Relapse Prediction
Survival analysis models (Cox regression, random survival forests) estimate relapse-free survival duration and predicted time to recurrence — enabling oncologists to calibrate surveillance intensity and follow-up scheduling.
Risk Stratification (Low / Moderate / High)
Patients are classified into actionable risk tiers based on model output — supporting clinical triage, resource allocation, and shared decision-making conversations between oncologists and patients.
Explainable Risk Factor Attribution
For each prediction, OncoSentinel™ surfaces the key contributing risk factors — including stage, tumor grade, lymph node involvement, treatment regimen, and prior relapse history — using SHAP values and feature importance outputs that clinicians can interpret and act on.
Comprehensive Input Variable Coverage
The model evaluates patient demographics (age, sex, race/ethnicity, BMI), cancer characteristics (type, histology, stage, grade, tumor size, metastatic status), treatment information (drug regimen, radiation, surgery, immunotherapy, targeted therapy), and clinical history (time since diagnosis, comorbidities, performance status). Biomarker integration (genomic markers, molecular signatures, imaging-derived features) is planned as a future enhancement.
Clinical Validation & Regulatory Documentation
Three-tier validation strategy: internal validation (train/test split, cross-validation, hyperparameter optimization), external validation (independent populations, multi-center datasets, multiple cancer types), and clinical performance assessment (AUC, sensitivity, specificity, PPV, NPV, calibration analysis, C-index). Full documentation suitable for regulatory and payer review.
Real-World Applications
An oncology practice needs to identify which post-treatment patients are at highest risk of relapse to prioritize surveillance imaging and follow-up visits.
OncoSentinel™ stratifies the post-treatment population into Low / Moderate / High risk tiers with explainable contributing factors — enabling oncologists to concentrate intensive surveillance on the patients who need it most.
A pharmaceutical company running a Phase III oncology trial needs a validated relapse prediction model to support adaptive design and endpoint selection.
OncoSentinel™ provides 1-year, 3-year, and 5-year relapse probability estimates with full validation documentation — supporting adaptive trial design protocols and FDA submission packages.
A cancer registry or academic medical center wants to use retrospective patient data to build and validate a multi-cancer relapse prediction tool.
OncoSentinel™ is developed using retrospective datasets from academic institutions, public oncology repositories, and cancer registries — with a validation framework designed for multi-center, multi-cancer-type deployment.
A health system wants to integrate AI-driven relapse risk scores into oncology care pathways to support shared decision-making and patient counseling.
OncoSentinel™ outputs risk category, probability, time-to-relapse estimate, and key risk factors in a clinician-interpretable format — designed for integration into oncology workflows and patient-facing care plans.
Ready to Discuss OncoSentinel™ — Clinical Relapse Risk Stratification?
Our team will assess your program context and design a solution scoped to your data environment, regulatory obligations, and timelines.