AI Radiomics for Immunotherapy Response Stratification
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Solution Overview
Problem
Existing non-invasive methods for determining immunotherapy response biomarkers and patient selection in immuno-oncology face challenges in accurately predicting treatment response without relying on predefined visual features and are not optimized for cancer prognosis, often requiring additional data and lacking robustness in differentiating patient responses.
Innovation Solution
A method utilizing deep learning and machine learning to extract radiomic features from medical images, such as CT and MRI, to predict immunotherapy response by training a Deep Neural Network (DNN) for 2D analysis and a Machine Learning (ML)-based classification, allowing for patient stratification based on immunotherapy response, with optional visual explanation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning models are trained on pre-defined radiomic features, then the model can be trained faster and with less computational resources, but the model lacks flexibility and cannot discover new predictive patterns
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models on large datasets of radiographic images to learn general radiomic feature representations. These pre-trained models can then be fine-tuned or applied directly to new datasets, eliminating the need for manual feature engineering while maintaining training efficiency. The model learns to extract relevant features automatically during the pre-training phase, resolving the contradiction between training speed and feature extraction flexibility.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the depth, width, and configuration of neural network layers to balance computational efficiency with feature extraction capability. By modifying network architecture parameters and training hyperparameters, the system achieves both fast training and the ability to discover novel predictive patterns in radiomic data.
2Ease of operation
If non-invasive radiomic methods are used to predict immunotherapy response, then patient recruitment and monitoring can be improved, but the methods lack robustness in differentiating between responder and non-responder populations
Solution Approach 1:
The patent applies segmentation by dividing the radiomic analysis into multiple specialized deep learning models, each trained to detect specific predictive patterns in different aspects of tumor imaging. Rather than relying on a single comprehensive model, the system segments the prediction task into multiple focused analyses that collectively improve differentiation between responder and non-responder populations, thereby enhancing reliability while maintaining ease of operation.
Solution Approach 2:
The patent implements feedback mechanisms where model predictions are continuously refined based on actual patient outcomes. Clinical response data is fed back into the training process to iteratively improve the model's ability to differentiate responders from non-responders. This feedback loop enhances prediction accuracy and robustness while maintaining the non-invasive nature of the monitoring approach.
3Reliability
If additional clinical data and follow-up information are collected to improve prediction accuracy, then the reliability of response prediction increases, but the complexity of data collection and processing increases
Solution Approach 1:
The patent applies universality by designing a multi-functional deep learning system that can process multiple types of data (radiographic images, clinical metadata, follow-up information) through a unified architecture. The same neural network framework handles diverse data inputs, reducing the need for separate processing systems for each data type and thereby managing complexity while maintaining high prediction accuracy through comprehensive data utilization.
Data Source
AI summary
A method of patient stratification between respondents and non-respondents to immuno-oncology (IO). This method, based on deep-learned features extracted owing to automatic AI-based models that have been fully-trained, goes beyond traditional radiomic standards, opening new perspective for a broader uptake of machine learning solutions in both patient care and drug development. Based on latest Machine Learning advances, the here proposed method allows predicting non-invasively a patient's tumor response to immuno-oncology therapy based treatment. The here proposed method operates not only on early stage conditions though a whole organ and lesion-agnostic analysis for prediction, but also on advanced metastatic stages through a multi-organ analysis performing a disease-agnostic and stage-agnostic prediction, potentially in accordance with response criteria defined by the RECIST 1.1 evaluation methodology.