Deep Learning Classifier for Immunotherapy Response Prediction
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Solution Overview
Problem
Current immunotherapy treatments using PD-1 and CTLA-4 checkpoint inhibitors have low response rates, high costs, and serious immune-mediated adverse events, necessitating more effective diagnostic tools to predict patient responses.
Innovation Solution
A multi-omic classifier is developed using deep learning analysis of imaging and clinical data, including diagnostic imaging scans, biomarkers, and clinical data to predict responses to PD-1/PD-L1 and CTLA-4 checkpoint blockade therapies in cancers such as non-small cell lung cancer, melanoma, and breast cancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If PD-1 and CTLA-4 checkpoint inhibitors are used for immunotherapy treatment, then treatment coverage is expanded, but response rates remain low and adverse events increase
Solution Approach 1:
The system performs preliminary analysis of imaging and clinical data before treatment to predict patient response to immunotherapy. By assessing imaging features and biomarkers in advance, the system identifies patients likely to respond, enabling proactive treatment selection and avoiding ineffective therapies.
Solution Approach 2:
The system introduces an intermediary predictive model that mediates between the treatment decision and patient outcome. This model integrates imaging and clinical data to generate a predicted response score, serving as an intermediate assessment tool that guides treatment selection and improves overall response rates.
2Adaptability or versatility
If immunotherapy treatment is administered, then treatment options are increased, but healthcare costs increase
Solution Approach 1:
The system extracts only the most relevant imaging features and clinical data elements needed for response prediction. By selecting and analyzing only the critical subset of data rather than all available information, the system reduces computational resources and associated costs while maintaining prediction accuracy.
Solution Approach 2:
The system uses readily available, low-cost imaging modalities (such as standard CT or MRI scans) and routine clinical biomarkers instead of expensive specialized tests. These conventional imaging and laboratory resources serve as cost-effective proxies for predicting immunotherapy response.
3Measurement precision
If comprehensive imaging and clinical data analysis is performed, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct components: imaging feature extraction, clinical data processing, and predictive modeling. Each component handles a specific aspect of data analysis independently, making the overall system more manageable and easier to implement while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The predictive model serves multiple functions simultaneously: it analyzes diverse data types (imaging and clinical), predicts treatment response, and can be applied across different patient populations and cancer types. This multi-functionality reduces the need for separate specialized systems for each analysis task.
Data Source
AI summary
A method includes training, using a processing device, an Artificial Intelligence (AI) model using training data associated with a plurality of patients to predict biologic therapy treatment responses indicative of a patient survival rate based on a change in volume of a lesion of a patient. The training data is indicative of at least one of unique diagnostic imaging scans at baselines, follow-up intervals, or temporary changes in lesion volume. The method includes providing a pre-treatment image of one or more target lesions of a target patient to the AI model to generate a biologic therapy treatment response. The method includes generating, based on the biologic therapy treatment response, a recommended treatment plan indicating a pharmaceutical product to treat the one or more target lesions of the target patient.