Automated TIL Graph Analysis for NSCLC Recurrence Prediction
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Solution Overview
Problem
Current methods for predicting the recurrence of non-small cell lung cancer (NSCLC) after surgical resection are slow, subjective, and lack accuracy, leading to ineffective treatments and unnecessary suffering, as they rely on manual evaluation by human pathologists and lack reliable, clinically relevant tools for determining the additional benefit of adjuvant chemotherapy.
Innovation Solution
An automated deep learning classifier is trained using digitized images of H&E stained slides to quantify tumor morphology and TIL spatial architecture, providing a continuous risk score that distinguishes patients likely to experience recurrence from those with longer disease-free survival, thereby facilitating personalized treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation by human pathologists is used to assess TIL density and grade tumors, then diagnostic accuracy may be achieved, but the process is slow, subjective, and error prone with inter-rater reliability issues
Solution Approach 1:
The patent replaces the manual mechanical process of human pathologists visually evaluating H&E stained images with an automated computer vision system. The system uses deep learning neural networks to automatically detect, count, and grade TILs from digital pathology images, eliminating human subjectivity and inter-rater variability while maintaining diagnostic accuracy and significantly reducing evaluation time.
Solution Approach 2:
The patent creates a digital copy of the H&E stained tissue images and processes these digital representations through automated algorithms. By working with digitized copies rather than physical slides requiring manual interpretation, the system achieves consistent, objective analysis that is both accurate and time-efficient.
2Reliability
If conventional predictive molecular tests are used to predict NSCLC recurrence, then some predictive ability is achieved, but they are expensive, involve tissue destruction, and require specialized facilities with long turnaround times
Solution Approach 1:
The patent extracts predictive information directly from the visual morphology and spatial architecture features of TILs in H&E stained images, eliminating the need for complex molecular tests. By focusing on the extractable visual characteristics of immune cell distribution and morphology, the system achieves predictive capability without requiring expensive molecular assays, specialized facilities, or extensive tissue processing.
Solution Approach 2:
The patent uses readily available H&E stained tissue sections and digital image data as disposable input materials, replacing expensive molecular tests. The system processes standard pathology slides that are already part of routine cancer diagnosis, making the predictive tool cost-effective and accessible without requiring specialized reagents or facilities.
3Productivity
If automated deep learning classification is used to predict NSCLC recurrence based on TIL features, then accuracy and speed are significantly improved, but the system requires sophisticated image processing and machine learning infrastructure
Solution Approach 1:
The patent segments the complex task of cancer recurrence prediction into distinct processing stages: image acquisition, TIL detection and counting, spatial architecture analysis, feature extraction, and classification. By dividing the sophisticated image processing into modular functional components, the system achieves high productivity while making the overall architecture more manageable and implementable.
Data Source
AI summary
Methods and apparatus predict non-small cell lung cancer (NSCLC) recurrence using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating NSCLC, a segmentation circuit that segments a cellular nucleus from the image, a feature extraction circuit that extracts a set of features from the image, a tumor infiltrating lymphocyte (TIL) identification circuit that classifies the segmented nucleus as a TIL or non-TIL, a graphing circuit that constructs a TIL graph and computes a set of TIL graph statistical features, and a classification circuit that computes a probability that the region will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A treatment plan may be provided based on the risk score.