Multimodal Clinical Predictor for Early Treatment Response Stratification
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Solution Overview
Problem
Current methods lack an accurate predictor for patient response to cancer treatments, particularly for predicting resistance to platinum-based chemotherapy in ovarian cancer and survival rates in lung cancer, which can lead to delayed consideration of alternative therapeutic options.
Innovation Solution
A machine learning model processes multimodal clinical data, including molecular data and biopsy image data, to predict patient response to treatments using supervised and unsupervised learning techniques, identifying highly correlated features and generating clinical predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard of care treatment is followed, then treatment is provided, but patient response prediction is inaccurate
Solution Approach 1:
The patent combines multiple data modalities (molecular data, biopsy image data, and clinical data) into a unified machine learning model to predict patient response to treatment. This merging of diverse data sources enables accurate prediction of treatment sensitivity or resistance, resolving the contradiction between providing standard treatment and achieving accurate response prediction.
2Loss of time
If treatment prediction is delayed, then standard treatment continues, but alternative options are considered too late
Solution Approach 1:
The machine learning model performs treatment response prediction before initiating or continuing standard treatment. By predicting patient sensitivity or resistance in advance, the system enables timely consideration of alternative therapeutic options, preventing the loss of critical time and improving survival outcomes.
3Measurement precision
If multimodal data is processed, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent processes different data modalities (molecular data, biopsy image data, clinical data) separately through dedicated processing pipelines before integrating them in the machine learning model. This segmentation approach manages computational complexity by handling each data type independently while maintaining high prediction accuracy through their combined analysis.
Data Source
AI summary
Methods and systems for performing a clinical prediction are provided. In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.