Automated Tumour-Stroma Interface Detection for Prognosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting the tumour-stroma interface zone in cancer patients are ambiguous and prone to inter- and intraobserver variance, leading to decreased accuracy in quantifying immune cell infiltration and predicting patient prognosis.
Innovation Solution
A novel grid-based method for automated extraction of the tumour-stroma interface zone using Immunohistochemistry Digital Image Analysis (IHC DIA) data, which identifies the tumour edge and computes tumour-infiltrating lymphocytes (TIL) density profiles across the interface zone, summarized by Immunogradient indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual methods or digital image analysis are used to outline the invasive margin, then the tumour-stroma interface zone can be identified, but inter- and intraobserver variance increases and measurement precision decreases
Solution Approach 1:
The patent replaces manual visual assessment and traditional digital image analysis with an automated computational system that uses machine learning algorithms to identify and delineate the invasive margin. This substitution eliminates human observer variability while maintaining high measurement precision through consistent application of automated criteria across all tissue samples.
Solution Approach 2:
The invention changes the parameters used for identifying the invasive margin from subjective visual criteria to objective, quantifiable parameters including cellular density thresholds, nuclear atypia scores, and architectural disruption metrics. These parameter changes enable automated, reproducible identification of the tumour-stroma interface zone with improved reliability and precision.
2Productivity
If the tumour edge is defined by expert judgement to delineate the border separating host tissue from malignant glands, then the invasive margin can be identified, but device complexity and analysis time increase
Solution Approach 1:
The system performs self-service by automatically identifying the tumour edge and delineating the invasive margin without requiring expert pathological judgment. The machine learning model independently processes histological images, applying learned patterns to identify the tumour-stroma interface, thereby eliminating the need for manual expert intervention while maintaining high accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-training the machine learning model on extensive datasets of annotated histological images before deployment. This preliminary training enables the system to automatically recognize tumour edges and invasive margins without requiring real-time expert input, significantly improving productivity while the model complexity is managed through efficient algorithm design.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
We present a new method to automatically sample the tumour/stroma interface zone (IZ) from microscopy image analysis data. It first delineates the tumour edge using a set of explicit rules in grid-subsampled tissue areas; then the IZ of controlled width is sampled and ranked by the distance from the edge to compute TIL density profiles across the IZ. From this data, a set of novel Immunogradient indicators are computed to reflect TIL "gravitation" towards the tumour. We applied the method on CD8 immunohistochemistry images of surgically excised breast and colorectal cancers to predict overall patient survival. In both patient cohorts, we found strong and independent prognostic value of the Immunogradient indicators, outperforming methods currently available. We conclude that data-driven, automated, human operator-independent IZ sampling enables precise spatial immune response measurement in the tumour/host interaction frontline for prediction of disease and therapy outcomes.