Tumor Geometric Feature Analysis for Biological Aggressiveness
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Solution Overview
Problem
Current methods for analyzing tumors lack effective means to determine biological aggressiveness and patient survivability based on geometric and spatial-temporal tissue architectural properties, which are crucial for accurate diagnosis and prognosis.
Innovation Solution
A system and method utilizing a computing device with machine learning components to analyze geometric shapes of tumors, extracting features such as circularity, eccentricity, and coherence between neighboring tumors, and outputting an indication of biological aggressiveness, trained on labeled datasets from H&E stained tissue slides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tumor analysis methods are used, then analysis simplicity is maintained, but measurement precision of biological aggressiveness is insufficient
Solution Approach 1:
The system segments tumor analysis into multiple geometric features (circularity, eccentricity, orientation angles, area ratios) that are calculated independently and then integrated by the machine learning model to determine biological aggressiveness, transforming a complex assessment into manageable component measurements
Solution Approach 2:
The patent introduces geometric shape parameters as intermediary measurements between traditional histological observation and biological aggressiveness assessment. These geometric features serve as mediators that bridge the gap between visual tumor morphology and quantitative prognosis prediction
2Reliability
If geometric feature extraction is implemented, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system replaces traditional manual pathological assessment with an automated computational approach using machine learning models that process geometric features, substituting human expert analysis with algorithmic evaluation to improve consistency and accuracy while managing computational complexity
Solution Approach 2:
The patent transforms qualitative histological observations into quantitative geometric parameters (circularity ratios, eccentricity values, orientation angles), changing the nature of the data from subjective visual assessment to objective numerical measurements that can be processed computationally
3Measurement precision
If spatial-temporal architectural properties are analyzed, then diagnostic precision is improved, but analysis time is increased
Solution Approach 1:
The system performs preliminary extraction of geometric features from histological images before applying the machine learning model for final prognosis prediction. This preliminary processing organizes the data in advance, reducing the computational burden during the actual prediction phase and streamlining the overall analysis workflow
Data Source
AI summary
Aspects of the present disclosure relate to systems and methods for analyzing a tumor, and more specifically, analyzing a tumor to identify a biological aggressiveness of the tumor. One example method for tumor analysis includes: receiving one or more input features associated with a geometric shape of a tumor, the one or more input features including at least a value indicating a circularity of the tumor; determining, via a machine learning component, a characteristic of the tumor based on the one or more input features, the characteristic being indicative of patient survivability; and outputting an indication of the characteristic.