Radiomics Classifier for NSCLM Immunotherapy Response Prediction
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Solution Overview
Problem
Current methods for evaluating response to PD-L1 checkpoint inhibitors like nivolumab in non-small cell lung cancer (NSCLC) are inadequate, often misclassifying patients as non-responders due to conventional radiological tools that underestimate tumor shrinkage and immune-related responses, leading to inappropriate treatment cessation.
Innovation Solution
The development of a machine learning classifier that uses quantitative imaging features from computed tomography (CT) images to predict patient response to immunotherapy by analyzing differences in radiomic features between pre-treatment and post-treatment images, providing a more accurate and reproducible assessment of treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional radiological tools (RECIST criteria) are used to evaluate treatment response, then the evaluation method is simple and widely applicable, but the measurement precision is insufficient leading to misclassification of pseudoprogressors
Solution Approach 1:
The patent transforms conventional qualitative radiological assessment into quantitative analysis by extracting multiple radiomic features (texture, shape, intensity) from CT images. This parameter transformation enables precise differentiation between true progression and pseudoprogression by analyzing subtle imaging characteristics beyond simple tumor diameter measurements.
Solution Approach 2:
The patent introduces a machine learning classifier as an intermediary between raw CT images and treatment response evaluation. This classifier processes radiomic features and generates predicted response probabilities, serving as a bridge that translates complex imaging data into clinically actionable insights while maintaining objective and reproducible assessment.
2Reliability
If conventional RECIST criteria are used, then the evaluation process is straightforward, but the reliability of treatment response prediction is poor due to misclassification of immune-related responses
Solution Approach 1:
The patent segments the tumor region in CT images to extract multiple radiomic features independently. By dividing the tumor into analyzable components and evaluating various imaging characteristics (texture homogeneity, edge definition, internal structure), the system achieves reliable differentiation between pseudoprogression and true progression without requiring complex external validation.
Solution Approach 2:
The patent implements a feedback mechanism where the machine learning classifier is trained on labeled data with known outcomes, continuously improving its prediction reliability. The system learns from historical treatment response data and refines its classification accuracy, creating a self-improving evaluation framework that enhances reliability over time.
3Measurement precision
If serial CT imaging with radiomics analysis is performed, then the prediction accuracy of treatment response is improved, but the loss of time for processing multiple images and features increases
Solution Approach 1:
The patent performs preliminary automated processing of serial CT images by extracting all necessary radiomic features in advance. The system pre-processes images to generate comprehensive feature sets that can be rapidly analyzed by the machine learning classifier, reducing the time required for final prediction while maintaining high accuracy through thorough feature extraction.
Solution Approach 2:
The patent creates a digital copy of the tumor's radiomic characteristics from serial CT images, transforming physical imaging data into computable feature representations. This digital replication allows rapid analysis and comparison of treatment effects without requiring repeated manual measurement or interpretation of original images, significantly reducing processing time while preserving measurement precision.
Data Source
AI summary
One embodiment include an image acquisition circuit that accesses a pre-treatment and a post-treatment image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), a segmentation and registration circuit that annotates the tumor represented in the images, and that registers the pre-treatment image with the post-treatment image; a feature extraction circuit that selects a set of pre-treatment and a set of post-treatment radiomic features from the registered image; a delta radiomics circuit that generates a set of delta radiomic features by computing a difference between the set of post-treatment radiomic features and the set of pre-treatment radiomic features; and a classification circuit that generates a probability that the region of tissue will respond to immunotherapy based on the difference, and that classifies the region of tissue as a responder or non-responder. Embodiments may generate an immunotherapy treatment plan based, at least in part, on the classification.