Deep Learning Prediction of Therapeutic Response Using Pre-treatment Imaging
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Solution Overview
Problem
Conventional approaches for predicting therapeutic agent response rely on serial imaging acquired during treatment, leading to potential selection of non-optimal treatment agents, delayed discovery of treatment failure, and adverse side effects, highlighting the need for optimizing prediction before treatment initiation.
Innovation Solution
The use of pre-treatment and intra-treatment serial imaging for predictive modeling, where changes in imaging features are extracted and analyzed using deep learning to predict therapeutic agent response, enabling earlier and more accurate selection of optimal treatment options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If serial imaging is acquired during treatment for prediction, then treatment response can be assessed, but treatment selection is delayed and non-optimal agents may be selected
Solution Approach 1:
The patent extracts imaging features from pre-treatment scans (before therapy initiation) and uses deep learning models to predict treatment response in advance. This preliminary analysis enables clinicians to select optimal treatment agents before treatment begins, avoiding delayed decision-making and exposure to non-optimal therapies.
2Measurement precision
If deep learning analysis of pre-treatment imaging is used, then treatment response prediction accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent extracts specific imaging features (radiomic features) from pre-treatment medical images such as CT or MRI scans. These extracted features serve as inputs to deep learning models, enabling accurate treatment response prediction while working with processed feature data rather than raw images, thus managing computational complexity.
3Reliability
If conventional serial imaging during treatment is used, then treatment monitoring is possible, but treatment failure is discovered late and adverse side effects occur
Solution Approach 1:
By performing deep learning analysis on pre-treatment imaging data, the system predicts which treatment agent is most likely to succeed before treatment begins. This advance prediction prevents clinicians from selecting ineffective agents, thereby avoiding adverse side effects associated with non-optimal treatment choices.
Data Source
AI summary
A system and method of using pre-treatment and intra-treatment serial imaging in at least one of predictive modeling or multi-modal predictive modeling of therapeutic agent response. The method includes acquiring pre-treatment features of one or more target lesions associated with a pre-treatment scan of a target subject prior to treating the target subject according to a treatment plan. The method includes determining a set of features indicative of a change in the one or more target lesions using the pre-treatment features. The method includes providing the set of features to one or more predictive models trained to predict therapeutic agent responses based on features of target lesions. The method includes generating a predicted treatment response score to for the treatment plan based on the set of features and the one or more predictive models prior to treating the target subject according to the treatment plan.