Digital Pathology Spatial Metrics for Tumor Infiltration Analysis
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Solution Overview
Problem
Current image-level analysis in digital pathology strips away detailed spatial information of biological objects, which is crucial for understanding their microenvironment and potential activities, hindering accurate diagnoses, prognoses, and treatment evaluations.
Innovation Solution
A digital pathology image processing system detects sets of biological object depictions and generates spatial-distribution metrics characterizing their locations and interrelations, using various analytical frameworks like spatial-point-process and geostatistical analysis, to provide subject-level results such as diagnoses, prognoses, and treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image-level analysis is used to simplify metadata storage and improve ease of operation, then ease of operation is improved, but spatial information and measurement precision are lost
Solution Approach 1:
The patent segments the image analysis into multiple levels: image-level results (simple counts and ratios) and object-level results (detailed spatial coordinates and distributions). This segmentation allows the system to maintain both ease of operation through simple metadata storage and preserve spatial information through detailed coordinate data, resolving the contradiction between simplicity and information retention.
Solution Approach 2:
The patent adds a spatial dimension to traditional image-level analysis by introducing object-level results that include x-y coordinates and spatial distributions. This dimensional expansion transforms the analysis from simple counting to spatially-aware characterization, preserving microenvironment information while maintaining the convenience of automated analysis.
2Productivity
If image-level analysis is used to simplify processing, then productivity is improved, but measurement precision and diagnostic accuracy deteriorate
Solution Approach 1:
The patent segments results into two categories: image-level results for quick overview and object-level results for precise measurement. This segmentation enables the system to maintain high productivity through automated image-level analysis while providing measurement precision through detailed object-level spatial data when needed for diagnostic accuracy.
Solution Approach 2:
The patent implements partial action by providing different levels of analysis detail based on needs. Image-level analysis provides sufficient information for routine cases, while object-level analysis provides excessive detail for cases requiring higher measurement precision, optimizing productivity without sacrificing accuracy when required.
3Measurement precision
If spatial distribution analysis is added to characterize microenvironment, then measurement precision and diagnostic accuracy are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the analysis system into modular components: image processing module, object detection module, spatial analysis module, and result generation module. This segmentation allows the system to implement complex spatial distribution analysis while managing device complexity through modular architecture, where each module handles a specific aspect of the analysis independently.
Solution Approach 2:
The patent introduces spatial metrics and object-level data structures as intermediaries between raw image data and clinical interpretations. These intermediaries organize complex spatial information into manageable formats (coordinates, distances, distributions) that can be processed efficiently, reducing the burden on the overall system complexity while maintaining measurement precision.
4Loss of information
If detailed object-level analysis is performed to capture spatial relationships, then loss of information is reduced, but processing time and computational requirements increase
Solution Approach 1:
The patent segments the processing pipeline into sequential stages: initial image-level analysis for quick results, followed by optional object-level analysis for detailed spatial information. This segmentation allows the system to minimize processing time for routine cases while capturing detailed spatial relationships when needed, reducing information loss without always incurring full processing time costs.
Solution Approach 2:
The patent applies partial action by performing detailed object-level analysis only when spatial information is clinically relevant. For routine cases, image-level analysis suffices, avoiding unnecessary processing time. For cases requiring microenvironment characterization, the system performs excessive analysis to ensure complete information capture, optimizing the balance between processing time and information retention.
Data Source
AI summary
Systems and methods relate to processing digital pathology images. More specifically, depictions of objects of a first class (e.g., lymphocytes) and depictions of objects of a second class (e.g., tumor cells) are detected. Locations of each biological object depiction are identified, which are used to generate multiple spatial-distribution metrics that characterize where depictions of objects of a first class are located relative to objects of a second class. The spatial-distribution metrics are used to generate a result corresponding to a predicted biological state of or a potential treatment of a subject. For example, the result may predict whether and/or an extent to which lymphocytes have infiltrated a tumor, whether checkpoint blockade therapy would be an effective treatment for the subject, and/or whether a subject is eligible for a clinical trial.