H&E Tissue Image Analysis for TIL Clustering and Recurrence Prediction
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Solution Overview
Problem
Existing methods for detecting tumor-infiltrating lymphocyte (TIL) subtypes in hematoxylin and eosin (H&E) stained tissue samples are complex, tissue-destructive, and ineffective in predicting cancer recurrence, particularly in non-small cell lung cancer (NSCLC), as they rely on quantitative immunofluorescence (QIF) or immunohistochemistry (IHC).
Innovation Solution
A non-destructive method using a machine vision approach with a Dirichlet Process Gaussian Mixture Model (DPGMM) and transfer learning-based CNN to identify and analyze TIL clusters in H&E images, extracting contextual and morphological features to predict cancer recurrence by clustering lymphocytes and quantifying their spatial distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantitative immunofluorescence (QIF) or immunohistochemistry (IHC) is used to detect TIL subtypes, then measurement precision is improved, but device complexity and tissue destruction increase
Solution Approach 1:
The patent uses deep learning models (U-Net, ResNet, EfficientNet) trained on H&E stained images to create a computational copy that can detect and classify TIL subtypes. This digital copying approach replaces the need for complex QIF/IHC procedures, achieving similar or better measurement precision without tissue destruction and with simpler implementation.
Solution Approach 2:
The patent replaces the mechanical/chemical processes of QIF and IHC with a computational vision system. Instead of using fluorescent dyes or chemical stains that require complex protocols, the system uses machine learning algorithms to analyze H&E stained images, substituting biological/chemical detection mechanisms with digital processing.
2Measurement precision
If QIF or IHC is used to analyze TILs, then measurement precision is improved, but loss of substance (tissue destruction) occurs
Solution Approach 1:
The deep learning models create a digital representation of TIL subtypes from H&E stained images, allowing repeated analysis without destroying the tissue sample. The computational model captures the essential features of TIL subtypes (CD3, CD4, CD8) through learned patterns in the image data, eliminating the need for destructive staining procedures.
3Quantity of substance
If existing TIL detection methods are used, then they can identify TIL density, but they fail to predict cancer recurrence accurately
Solution Approach 1:
The patent moves beyond measuring overall TIL density to analyzing the local spatial distribution and interplay between different TIL subtypes (CD3, CD4, CD8). The deep learning models detect patterns in the spatial arrangement and relationships between cell types, which provides much stronger predictive power for cancer recurrence while maintaining the ability to quantify TIL density.
Solution Approach 2:
The patent adds the dimension of spatial relationships and cellular interactions to the traditional one-dimensional density measurement. By analyzing the positional information and contextual relationships between different TIL subtypes in the tumor microenvironment, the system achieves superior prediction accuracy for cancer recurrence.
4Device complexity
If manual inspection of H&E stained tissue samples is used, then device complexity is reduced, but measurement precision and productivity are insufficient
Solution Approach 1:
The deep learning models are trained to automatically perform the analysis that would otherwise require manual inspection by pathologists. The system self-processes H&E stained images, detecting and classifying TIL subtypes without human intervention, thereby maintaining the simplicity of H&E staining while dramatically improving productivity and analysis speed.
Data Source
AI summary
The present disclosure relates to an apparatus including one or more processors configured to receive a digitized image of a region of tissue demonstrating a disease, and containing cellular structures represented in the digitized image, each of the cellular structures being associated with a cell category of a plurality of cell categories; select a cellular structure of the cellular structures based on the cell category for the cellular structure; for the cellular structure selected, compute a set of contextual features; assign, based on the set of contextual features, the cellular structure to at least one cluster of a plurality of clusters; compute cluster features, the cluster features describing characteristics of the at least one cluster of the plurality of clusters; and generate a prediction that describes a pathologic or phenotypic state of the disease based, at least in part, on the cluster features and/or the set of contextual features.