Multimodal Treatment Response Prediction With Missing Data Imputation
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Solution Overview
Problem
Current methods for predicting treatment response in cancer patients, particularly for immunotherapy in NSCLC, are limited by the suboptimal predictive power of biomarkers like PD-L1 expression, leading to high costs and ineffective treatment for many patients, and the challenge of handling missing data in clinical settings hampers the implementation of multimodal prediction models.
Innovation Solution
A computer-implemented method using a combination of trained imputation and prediction machine learning models to process multimodal features from clinical, biological, and radiological data, including imputation of missing data and calculation of feature changes over time, to accurately predict treatment response or efficacy for individual patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single biomarker prediction methods (e.g., PD-L1 expression) are used, then the predictive model is simple and easy to implement, but the predictive accuracy is suboptimal
Solution Approach 1:
The patent combines multiple data modalities including clinical data, radiological data, and genomic data into an integrated multimodal predictive model. This merging of diverse data sources enhances prediction accuracy by capturing complementary information that single biomarker methods miss, directly resolving the contradiction between model simplicity and predictive accuracy.
2Measurement precision
If multimodal data integration is implemented, then the treatment response prediction accuracy is improved, but the model complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a missing data imputation model as an intermediary component that preprocesses incomplete data before it enters the main prediction model. This mediator handles the complexity of missing data systematically, enabling the multimodal integration to proceed smoothly without overwhelming computational burden, thus managing model complexity while maintaining accuracy.
Solution Approach 2:
The patent performs missing data imputation as a preliminary step before feeding data into the prediction model. By preprocessing the data to fill gaps in advance, the system reduces the computational complexity during the main prediction phase, allowing multimodal integration to achieve high accuracy without excessive real-time processing requirements.
3Measurement precision
If complete patient data is required for prediction, then the prediction accuracy is maximized, but the applicability to real-world clinical settings with missing data is reduced
Solution Approach 1:
The patent converts the harmful effect of missing data into a benefit by using imputation models that leverage relationships between different data modalities. Instead of treating missing data as a limitation, the system uses available data from other modalities to infer and fill gaps, thereby maintaining high prediction accuracy while significantly improving applicability to real-world clinical settings where incomplete data is common.
4Loss of information
If multiple data modalities are integrated, then the comprehensive understanding of treatment response is improved, but the data processing time and computational resources increase
Solution Approach 1:
The patent performs missing data imputation and data preprocessing as preliminary actions before the main prediction computation. By preparing and completing the data set in advance, the system reduces the computational burden during actual prediction, thereby maintaining comprehensive information from multiple modalities while reducing real-time data processing time for clinical deployment.
Data Source
AI summary
The present invention is directed to a computer-implemented method of predicting treatment result (treatment response or treatment efficacy of a patient) based on the patient's multimodal features collected at least at two different time points. In particular, the invention relates to methods for predicting lung cancer patients' response to immunotherapy treatment.