Multi-scale virtual cancer model for personalized therapy prediction
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Solution Overview
Problem
Current cancer treatment methods often result in ineffective therapies due to poor predictive power, as they rely on broad disease classifications rather than individual patient responses, leading to unnecessary chemotherapy and limited ability to assess or predict a patient's response to therapy.
Innovation Solution
A multi-scale complex system that integrates molecular, cellular, tissue, and organismic measurements to create personalized virtual cancer models, allowing for the prediction of cancer progression and response to therapy by correlating individual measurements with known outcomes and incorporating virtual perturbations such as drugs or environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If broad disease classifications are used for cancer treatment, then treatment simplicity is improved, but predictive power of individual patient response deteriorates
Solution Approach 1:
The patent segments cancer treatment prediction into multiple scales: molecular-scale measurements (genetic, proteomic), cellular-scale measurements, tissue-scale measurements, and organism-scale measurements. This segmentation allows comprehensive characterization of individual patient responses while maintaining systematic organization, resolving the contradiction between treatment simplicity and predictive precision.
Solution Approach 2:
The patent introduces a multi-dimensional measurement framework that adds spatial and organizational dimensions to cancer assessment. By measuring across multiple scales (molecular, cellular, tissue, organism) simultaneously, the system transforms a single-dimension classification approach into a multi-dimensional predictive model, thereby improving predictive power without sacrificing operational feasibility through integrated analysis.
2Measurement precision
If multiple measurements are taken to improve prediction accuracy, then predictive power is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple measurement systems across different scales into a unified multi-scale complex system. By integrating molecular, cellular, tissue, and organism-scale measurements into a single predictive framework, the patent reduces the effective complexity of managing separate systems while maintaining the comprehensive data collection needed for high predictive power.
Solution Approach 2:
The patent creates a universal predictive platform that can handle diverse measurement types across multiple scales. This multi-functional system uses common analytical approaches and integration methods applicable to various measurement modalities, thereby managing system complexity through standardized processes while maintaining measurement precision across all scales.
3Adaptability or versatility
If personalized virtual models are created for each patient, then treatment personalization is improved, but computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by creating personalized virtual cancer models before treatment decisions are made. These virtual models incorporate patient-specific measurements across multiple scales and pre-compute treatment response predictions, allowing clinicians to make informed decisions without requiring intensive real-time computational resources during actual treatment planning.
Solution Approach 2:
The patent creates virtual copies of patient-specific cancer systems that can be simulated computationally. These simplified virtual models replicate the essential characteristics of individual patient cancers at multiple scales, enabling treatment prediction through simulation rather than requiring complex real-time calculations on actual patient data.
Data Source
AI summary
Described herein are methods and systems to measure dynamics of disease progression, including cancer growth and response, at multiple scales by multiple techniques on the same biologic system. Methods and systems according to the invention permit personalized virtual disease models. Moreover, the invention allows for the integration of previously unconnected data points into an in silico disease model, providing for the prediction of disease progression with and without therapeutic intervention.