AI-Driven Precision Dosing and Survival Analytics for Personalized Brain Radiosurgery

This invention is an advanced clinical decision support system that utilizes machine learning to determine optimal radiotherapy doses and predict survival outcomes for patients undergoing brain radiosurgery. By synthesizing complex subject-specific data, the technology empowers clinicians to deliver highly personalized treatment plans that maximize tumor control while minimizing the risk of adverse side effects.

Description

The technology functions as a comprehensive data processing platform that identifies and analyzes a wide array of patient data points, including age, tumor subtype, location, and previous treatment history. At its core, the system employs a trained machine learning model, such as a Feedforward Neural Network or Gradient Boosting Regressor, to process these inputs and generate specific clinical metrics. Specifically, the model calculates a recommended radiation dose (measured in Gray) tailored to the unique characteristics of the target feature, such as a brain tumor or lesion. The system further distinguishes itself by incorporating "guardrails" that enforce real-world safety standards, ensuring recommendations remain within clinically plausible ranges. Beyond initial dosing, the platform provides predictive analytics regarding a patient's likelihood of overall and progression-free survival over intervals ranging from one week to ten years. This dual capability of optimizing treatment parameters and forecasting outcomes allows for a closed-loop feedback mechanism that significantly improves the precision of radiosurgery administration.

Applications

- Clinical Decision Support Software: Integration into hospital oncology departments to assist neurosurgeons and radiation oncologists in treatment planning.
- Radiosurgery System Enhancements: A value-added software component for manufacturers of Gamma Knife or Linear Accelerator (LINAC) system.
- Personalized Survival Analytics: Tools for health insurance providers and medical researchers to model long-term patient outcomes and treatment efficacies.
- Adverse Effect Risk Assessment: Specialized modules for predicting specific risks, such as post-treatment hearing loss or vestibular symptoms in schwannoma patients.
- Telemedicine & Remote Planning: An offline-capable application that allows for sophisticated treatment modeling in diverse clinical environments.

Advantages

- Enhanced Precision Dosing: Provides individualized radiation dose recommendations based on a subject's unique clinical profile rather than generalized guideline.
- Improved Patient Outcomes: Optimizes dosage to increase the likelihood of effective treatment and effective control of brain conditions.
- Reduced Clinical Risks: Lowers the probability of complications like over-treatment or under-treatment by utilizing predictive feedback and safety guardrails.
- Prognostic Decision Support: Assists clinicians in discussing long-term survival expectations and designing personalized post-treatment surveillance plans.
- Computational Efficiency: Generates complex clinical predictions within seconds, allowing for rapid integration into existing hospital workflows

Invention Readiness

The technology is currently a fully functional, offline clinical decision support application. Internal validation has been performed using clinical datasets, demonstrating strong predictive accuracy for prescription doses (R^2 ~ 0.63) and post-treatment hearing loss (AUC ~ 0.89). While the model generalizes well across multiple treatment profiles, further studies are required; a prospective clinical validation study and an external pilot are planned to evaluate real-world usability, safety, and the direct impact on physician planning decisions. Additional work is also needed to refine predictions for progression-free survival and specific vestibular symptoms through the inclusion of richer dosimetric and audiometric variables.

IP Status

Patent Pending