Radiomic Heterogeneity Markers for CDK4/6 Response Prediction
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Solution Overview
Problem
Current treatments with CDK 4/6 inhibitors for hormone receptor-positive (HR+) metastatic breast cancer face challenges such as resistance development, high cost, and severe side effects, with no reliable molecular markers for predicting therapeutic response or resistance, leading to uncertainty in treatment effectiveness and safety.
Innovation Solution
Utilizing radiomic heterogeneity features from imaging data to extract prognostic markers that predict the effectiveness of CDK 4/6 inhibitor therapy by analyzing texture features within metastatic lesions, employing machine learning models to generate radiomic risk scores and risk groups, thereby guiding treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CDK 4/6 inhibitors are used to treat HR+ metastatic breast cancer, then therapeutic effectiveness is improved, but resistance development and severe side effects occur
Solution Approach 1:
The patent extracts radiomic heterogeneity features from baseline imaging data before treatment initiation to predict which patients will respond to CDK 4/6 inhibitors. This preliminary assessment allows clinicians to identify suitable candidates in advance, avoiding ineffective treatment and its associated side effects and resistance risks for non-responders.
Solution Approach 2:
The patent introduces radiomic risk scores as an intermediary biomarker that mediates between imaging data and treatment outcome prediction. These scores serve as a proxy to identify patients who will benefit from CDK 4/6 inhibitors without requiring direct observation of treatment response over time, enabling early selection of appropriate candidates.
2Productivity
If CDK 4/6 inhibitors are administered to all HR+ metastatic breast cancer patients, then more patients may benefit, but cost and unnecessary exposure to side effects increase
Solution Approach 1:
The patent applies local quality by identifying specific subgroups of patients with particular radiomic heterogeneity characteristics who are most likely to respond to CDK 4/6 inhibitors. Instead of uniform treatment for all HR+ patients, the approach tailors treatment selection to the specific radiomic profile of each patient's tumor, optimizing benefit while reducing unnecessary exposure.
3Measurement precision
If molecular markers are used to predict treatment response, then personalized treatment can be achieved, but no reliable molecular markers currently exist for CDK 4/6 inhibitors
Solution Approach 1:
The patent replaces the nonexistent reliable molecular marker system with an imaging-based radiomic analysis system. By extracting heterogeneity features from standard imaging data and generating radiomic risk scores, the invention substitutes the failed molecular marker approach with a functional imaging biomarker that provides predictive information about treatment response.
Data Source
AI summary
The present disclosure relates to a method of determining a prognostic outlook for patients having metastatic breast cancer. The method includes receiving imaging data from an image of a patient that is receiving or that is to receive cycline dependent kinase 4 and 6 (CDK 4/6) inhibitor therapy for hormone receptor-positive (HR+) metastatic breast cancer. Radiomic heterogeneity features are extracted from imaging data associated with a metastasis within the imaging. A prognostic marker is determined from the radiomic heterogeneity features. The prognostic marker is indicative of a response of the patient to CDK 4/6 inhibitor therapy for HR+ metastatic breast cancer.