Information Processing for Spatial Biomarker Prognosis Estimation
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Solution Overview
Problem
Existing prognosis prediction methods for diseases, particularly those involving CD4-positive and CD8-positive T cell lymphocytes, suffer from limited information availability and low prediction accuracy.
Innovation Solution
An information processing apparatus that utilizes a prognosis estimation model trained on spatial distributions of biomarkers and proteins in patient specimens to improve prediction accuracy by inputting spatial distribution data, including features like CD3-positive and CD20-positive lymphocytes, and optionally drug information, to estimate patient prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prognosis prediction methods are used, then the method is simple and easy to implement, but the prediction accuracy is low due to limited information availability
Solution Approach 1:
The patent transitions from traditional bulk tissue analysis to spatial distribution analysis by introducing spatial coordinates as a new dimension. The system analyzes the spatial arrangement of lymphocytes and tumor cells within tissue sections, capturing heterogeneity that conventional methods miss. This dimensional expansion enables more accurate prognosis prediction by incorporating spatial information alongside traditional biomarker data.
Solution Approach 2:
The patent segments the tissue sample into multiple spatial regions and analyzes different cell types (CD4+ T cells, CD8+ T cells, tumor cells) separately while considering their spatial relationships. By dividing the complex tissue structure into manageable spatial units and analyzing each region's characteristics, the system achieves comprehensive prognosis assessment without overwhelming complexity.
2Measurement precision
If spatial distribution data is collected and analyzed, then prediction accuracy improves, but the complexity of data acquisition and processing increases
Solution Approach 1:
The patent introduces an intermediary processing layer that bridges the gap between complex spatial data acquisition and prognosis prediction. The system first processes image data from tissue sections to extract spatial distribution information, then feeds this processed data into the prognosis estimation model. This intermediary step simplifies the overall process by pre-processing and structuring the complex spatial data before final analysis.
Solution Approach 2:
The system employs a multi-functional platform that can process various types of spatial distribution data (different biomarkers, cell types, tissue regions) through a unified framework. The same core algorithm handles diverse input data formats and analytical requirements, reducing the complexity burden for different measurement scenarios while maintaining high prediction accuracy across various applications.
Data Source
AI summary
An information processing apparatus includes a storage that stores a prognosis estimation model, which has been trained to output a prognosis of a patient upon input of input data including a spatial distribution of at least one of a feature value related to a predetermined biomarker or a predetermined protein in a specimen collected from the patient, an acquisition part that acquires a spatial distribution of at least one of a feature value related to a predetermined biomarker and a predetermined protein in a specimen collected from a target patient, and a prognosis estimation part that outputs, as an estimated value of a prognosis of the target patient, information output by inputting input data including the spatial distribution acquired by the acquisition part to the prognosis estimation model.