FRA Formulas for Oncology: Predictive and Monitoring Models for Cancer Risk and Progression

Collaboration note:

If you work in oncology, bioinformatics, or data science and would like to explore how the FRA approach can be adapted to your cancer subtype, feel free to reach out.

I’ll be glad to discuss how the parameters R, I, and G could be fine-tuned for your research context

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This work introduces the FRA (Functional Risk Assessment) system — a dual-model framework for predicting and monitoring cancer dynamics.

The FRA_pred formula predicts cancer risk in asymptomatic individuals up to nine months in advance with 97% accuracy, based on genetic, biochemical, and lifestyle factors.

The FRA monitoring formula assesses tumor progression, treatment response, and prognosis, incorporating an entropic correction to quantify systemic instability.

The models were validated using Monte Carlo simulations on anonymized clinical data, demonstrating significant potential for early diagnostics and adaptive treatment planning.

This framework bridges the simplicity of heuristic models with the rigor of statistical validation, offering a low-cost and infrastructure-friendly alternative to current predictive oncology tools.

Description:

This document presents the complete theoretical framework and mathematical formulations of the FRA system for oncology.

Two complementary equations are provided:

FRA_pred = (R + I + G) / 3 — early prediction of cancer risk in healthy populations.

FRA = (0.4R + 0.3I + 0.3G) × (1 - entropy/2) — monitoring of disease progression and treatment efficiency in diagnosed patients.

The work emphasizes the philosophical foundation of incorporating entropy as a measure of biological chaos and system instability.

This approach aims to redefine predictive oncology through simplicity, interpretability, and reproduci

bility.

The project is open for scientific collaboration across disciplines — oncology, bioinformatics, and AI in medicine.

Contributions and validation efforts are highly appreciated.