Digital Pathology Nuclei Morphology for Therapy Response Prediction
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Solution Overview
Problem
The variability in patient response to immune checkpoint inhibitor therapies, such as anti-PD-(L)1 treatments, is a significant challenge in cancer therapy, is a significant challenge in cancer therapy, as existing technologies have not been effectively addressed, and improved biomarkers to identify the patients most likely to benefit from these therapies are needed for better treatment decision-making and improved healthcare outcomes.
Innovation Solution
Utilizing trained machine learning models to predict therapeutic response by analyzing morphological parameters of tumor cell nuclei, such as perimeter, area, and other parameters, in an image of a patient sample, and generating a prediction of a therapeutic response, such as anti-PD-(L)1 treatment, and administering the specified treatment, such as atezolizumab, based on the response to the patient, by analyzing the patient's tumor specimen images and associated clinical data, and generating a therapeutic response score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional biomarkers are used to predict therapeutic response, then treatment decisions can be made, but prediction accuracy is insufficient leading to variable patient outcomes
Solution Approach 1:
The patent segments the tumor tissue into individual cell nuclei and further into morphological features (area, perimeter, circularity, etc.). This segmentation allows extraction of multiple independent morphological parameters from each nucleus, which are then aggregated into statistical features. This fine-grained segmentation enables more precise prediction of therapeutic response compared to traditional bulk biomarkers.
Solution Approach 2:
The patent transitions from traditional one-dimensional biomarker measurements (e.g., PD-L1 expression levels) to multi-dimensional morphological feature space. By extracting numerous morphological parameters (area, perimeter, circularity, eccentricity, etc.) and their statistical measures (mean, median, standard deviation, skewness, kurtosis) across different spatial scales, the system creates a high-dimensional feature representation that captures complex tumor heterogeneity, thereby improving prediction accuracy.
2Measurement precision
If comprehensive patient data analysis is performed to improve prediction accuracy, then better treatment decisions can be made, but computational complexity and processing time increase
Solution Approach 1:
The computational system is segmented into distinct functional modules: image preprocessing module, nucleus segmentation module, morphological feature extraction module, statistical aggregation module, and prediction module. This segmentation allows each module to be optimized independently and facilitates parallel processing, reducing overall computational complexity while maintaining comprehensive data analysis.
Solution Approach 2:
The system performs preliminary actions by pre-processing images (color normalization, artifact removal) and pre-segmenting nuclei before the actual prediction task. Morphological features are pre-computed and stored for each nucleus, and statistical aggregates are pre-calculated across different spatial scales. This preliminary processing organizes data in advance, reducing the computational burden during the actual therapeutic response prediction phase.
3Measurement precision
If detailed morphological analysis of tumor cell nuclei is performed, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively computing morphological features at multiple spatial scales rather than analyzing every single nucleus in detail. It calculates statistical measures (mean, median, standard deviation, skewness, kurtosis) for morphological parameters across different spatial hierarchies (individual nuclei, clusters, regions, entire tissue sections). This partial yet comprehensive approach captures essential tumor heterogeneity while avoiding exhaustive analysis of every cellular detail, thus reducing processing time.
Solution Approach 2:
Morphological features such as area, perimeter, circularity, and eccentricity are pre-computed for each nucleus during the segmentation phase. These pre-computed features are then reused across multiple statistical aggregations and prediction models, avoiding redundant calculations. This preliminary feature extraction significantly reduces the computational time required for subsequent analysis while maintaining comprehensive morphological characterization.
Data Source
AI summary
Systems and methods for predicting the therapeutic response of a specified disease therapy for individual patients based on an analysis of digital pathology images are described. In some instances, for example, the disclosed methods can comprise: receiving an image of a tumor specimen from a patient; segmenting the image to identify tumor cell nuclei; generating a feature vector that includes a plurality of features, each corresponding to a statistical measure of one of a set of morphological parameters used to characterize the tumor cell nuclei; and providing the generated feature vector as input to a trained machine-learning model configured to output a prediction of the therapeutic response of the specified disease therapy for the patient.