Nuclear Cell Graphs for Immunotherapy Response Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current biomarkers for predicting response to immune checkpoint inhibitors in non-small cell lung cancer (NSCLC) are inadequate, resulting in only approximately 20% response rate, necessitating a more accurate method to identify patients who will benefit from these treatments.
Innovation Solution
A method involving deep learning to segment cellular nuclei in H&E stained images, extracting nuclear shape and texture features, constructing cell graphs, and using machine learning classifiers to predict response to immunotherapy by analyzing digitized tissue images, specifically using a quadratic discriminant analysis (QDA) classifier with selected radiomic and graph features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tissue-based PD-L1 expression detection is used as the biomarker, then the current gold standard method is available, but the response rate is only approximately 20% and the biomarker is inadequate
Solution Approach 1:
The patent replaces the conventional immunohistochemistry (IHC) method with a computational image analysis system using deep learning and machine learning algorithms. The system processes H&E stained tissue images to extract nuclear features and predicts immunotherapy response, substituting the mechanical/chemical detection process with an information processing system that achieves superior predictive accuracy.
Solution Approach 2:
The patent transforms the approach by changing from detecting PD-L1 protein expression levels to analyzing nuclear morphological and textural parameters. By extracting multiple features including nuclear area, perimeter, shape factors, and texture metrics from H&E images, the system creates a multi-parameter predictive model that outperforms single-parameter PD-L1 detection.
2Measurement precision
If more accurate prediction methods are developed, then patient identification accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent divides the complex prediction task into distinct segments: (1) image preprocessing and normalization, (2) nuclear segmentation and boundary detection, (3) feature extraction (morphological and textural), (4) graph construction representing spatial relationships, and (5) machine learning classification. This modular segmentation manages complexity while maintaining high predictive accuracy.
Solution Approach 2:
The patent introduces intermediate representations including cell graphs that model spatial relationships between nuclei, and feature vectors that bridge the gap between raw image data and predictive outcomes. These intermediaries organize complex information in structured formats that facilitate accurate prediction without requiring direct complex processing of raw images.
Data Source
AI summary
Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.