Quantitative Textural Analysis for Lung Cancer Immune Therapy Prediction
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Solution Overview
Problem
Current methods for evaluating tumor biology in lung cancer patients for immune therapy responsiveness are invasive and lack real-time, objective assessment, with existing imaging analysis techniques being qualitative and prone to variability.
Innovation Solution
A biomarker signature is derived from quantitative textural analysis (QTA) of imaging data, using logistic regression modeling to predict immune therapy responsiveness in lung cancer patients by processing CT scan data and generating metrics such as mean pixel density, standard deviation, entropy, skewness, and kurtosis, which are then used to compare and predict patient responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive tissue analysis is used to evaluate tumor biology, then measurement precision is improved, but loss of time increases due to the delay in obtaining interpretable results
Solution Approach 1:
The patent replaces invasive mechanical tissue sampling with non-invasive imaging-based quantitative textural analysis. CT imaging data is processed to extract textural features that serve as biomarkers for tumor biology, eliminating the need for physical tissue extraction while providing real-time assessment without the weeks-long delay associated with pathological analysis.
Solution Approach 2:
The patent creates a virtual copy of tissue characteristics through imaging phenotypes. Quantitative textural analysis of CT images generates biomarker signatures that replicate the information obtained from invasive tissue analysis, allowing assessment of tumor biology, angiogenesis, and metabolism through image-based proxies rather than physical samples.
2Ease of operation
If qualitative imaging analysis is used to assess tumor appearance, then ease of operation is improved, but measurement precision deteriorates due to interpretation variability
Solution Approach 1:
The patent transforms qualitative visual assessment into quantitative measurement by extracting specific textural parameters from imaging data. Instead of relying on subjective interpretation of tumor appearance, the system calculates objective biomarker signatures from textural features, converting qualitative observations into precise, reproducible numerical metrics that eliminate reader variability.
Solution Approach 2:
The patent replaces human visual interpretation with automated computational analysis. Quantitative textural analysis algorithms process imaging data to generate biomarker signatures, substituting subjective qualitative assessment with objective computer-based measurement that provides consistent, reproducible results across different observers.
3Ease of manufacture
If conventional imaging parameters are used for tumor assessment, then ease of manufacture is improved, but measurement precision worsens due to limited biological correlation
Solution Approach 1:
The patent segments the tumor region into multiple sub-regions and extracts textural features from each segment. By dividing the tumor into smaller units and analyzing textural characteristics of individual segments, the system captures heterogeneous biological signals within the tumor that conventional whole-tumor measurements miss, improving the precision of biological assessment.
Solution Approach 2:
The patent adds a new dimension of analysis by extracting textural features from imaging data that conventional parameters do not capture. Instead of relying solely on standard measurement dimensions like size and shape, the system incorporates textural dimensionality that reflects underlying biological heterogeneity, providing more precise measurement of tumor biology.
Data Source
AI summary
Methods and apparatus for predicting responsiveness to immune therapy in lung cancer. The method includes the steps of: identifying a first population of known responders and a second population of known non-responders; processing imaging data for the first and second populations using quantitative textural analysis (QTA); generating, for each member of both populations, quantitative metrics using the QTA; performing logistic regression on the quantitative metrics for both populations to yield a predictive signature expressed in the form of Y=Mx+B where x comprises mean pixel density; performing QTA on a lung cancer scan for a subsequent patient; comparing the predictive signature to one or more relevant metrics associated with the subsequent patient; and predicting responsiveness to immune therapy for the subsequent patient based on the comparison.