Tumor Classification via Immune Cell Proximity Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for tumor classification, such as MSS/MSI status analysis, are time-consuming, expensive, and less accurate compared to image-based approaches that assess the proximity of immune cells to tumor cells, which is crucial for determining the inflammatory status and prognosis of tumors.
Innovation Solution
An image analysis method that receives digital images of tissue samples, identifies immune and tumor cells, computes a proximity measure based on the distance between them, and classifies tumors as inflammatory or non-inflammatory, providing a more accurate prognosis and treatment recommendations by analyzing the spatial relationship between immune and tumor cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genetic analysis methods (MSS/MSI status) are used for tumor classification, then comprehensive genetic information can be obtained, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces complex genetic sequencing analysis with an image-based computational approach. Instead of performing time-consuming next-generation sequencing to determine MSS/MSI status, the system uses digital image processing of histological sections to identify and measure immune cell proximity to tumor cells, providing classification results much faster while maintaining diagnostic relevance
Solution Approach 2:
The patent creates a visual proxy for genetic status by analyzing the spatial distribution of immune cells in tissue images. Rather than directly measuring genetic mutations, the system captures the phenotypic expression of immune response patterns that correlate with MSS/MSI status, providing an indirect but clinically useful measurement through image analysis
2Measurement precision
If genetic sequencing is performed to determine MSS/MSI status, then tumor genetic characteristics can be identified, but the cost increases significantly
Solution Approach 1:
The patent employs standard histological staining and routine microscopy techniques that are already widely available in clinical settings, replacing expensive next-generation sequencing infrastructure. The method uses conventional tissue sections and common immune cell markers that can be visualized with standard immunohistochemistry, dramatically reducing the cost barrier while providing actionable classification data
3Reliability
If MSS/MSI status analysis is used for tumor classification, then genetic stability information can be obtained, but the prognostic accuracy is reduced compared to immune cell proximity assessment
Solution Approach 1:
The patent shifts from global genetic characterization to local spatial analysis. Instead of determining overall tumor genetic stability through bulk sequencing, the method examines the specific microenvironment around individual tumor cells, measuring the distance to nearest immune cells. This local assessment provides more granular and prognostically relevant information about the actual immune response at the tumor-immune interface
Data Source
AI summary
At least one embodiment relates to an image analysis system for tumor classification. The system is configured for receiving at least one digital image of a tissue sample; analyzing the at least one received image for identifying immune cells and tumor cells in the at least one received image; for each of the identified tumor cells, determining the distance of the tumor cell to the nearest immune cell; computing a proximity measure as a function of the determined distances; in dependence on the proximity measure, classifying the identified tumor cells into tumor cells of an inflammatory tumor or as tumor cells of a non-inflammatory tumor; and storing the classification result on a storage medium and/or displaying the classification result on a display device.