NSCLC Recurrence Prediction via TIL Spatial Architecture Analysis
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Solution Overview
Problem
Existing approaches to estimating tumor-infiltrating lymphocyte (TIL) density in non-small cell lung cancer (NSCLC) suffer from inter-reader variability, limiting their clinical utility as prognostic markers, and primarily focus on counting individual TILs or estimating TIL grade, which is sub-optimal for predicting recurrence.
Innovation Solution
The use of quantitative features related to the spatial architecture of TILs, co-localization with cancer nuclei, and density variation from H&E images, employing graph network algorithms and machine learning classifiers to predict recurrence with improved accuracy and repeatability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual estimation of TIL density by pathologists is used, then clinical assessment can be performed, but inter-reader variability is high and measurement precision is limited
Solution Approach 1:
The patent replaces manual visual estimation by pathologists with an automated image processing system that uses H&E stained slide images, graph network algorithms, and machine learning classifiers to objectively quantify TIL density and spatial architecture, eliminating inter-reader variability and improving measurement precision
Solution Approach 2:
The patent transforms the assessment from simple TIL density counting to a multi-parameter analysis including spatial architecture features, co-localization metrics, and density variation parameters extracted through automated image analysis, thereby improving both precision and reliability
2Measurement precision
If simple TIL counting or grade estimation is used, then the process is simple and quick, but predictive accuracy for recurrence is limited
Solution Approach 1:
The patent segments the TIL assessment into multiple independent analytical components: individual TIL detection, cluster identification, spatial relationship analysis, and co-localization with cancer nuclei, allowing complex predictive features to be extracted systematically while maintaining analytical rigor
Solution Approach 2:
The patent transitions from two-dimensional simple counting to multi-dimensional analysis by incorporating spatial coordinates, cluster density distributions, and three-dimensional spatial relationships between TILs and cancer nuclei, thereby significantly improving recurrence prediction accuracy
3Measurement precision
If automated image analysis with graph network algorithms is used, then measurement precision and repeatability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements self-service through automated machine learning classifiers and graph network algorithms that autonomously perform TIL detection, classification, and spatial analysis without requiring manual pathologist intervention, thereby achieving high precision while the system manages its own complexity through automation
Data Source
AI summary
Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.