Cancer Classification Mapping via Lookup Table Segmentation
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Solution Overview
Problem
Current cancer classification systems, such as ICD-O and SNOMED-CT, are not designed to support computational needs for clinical decision support systems and struggle to incorporate new genomic alterations, particularly in rare tumors, leading to inefficiencies in data integration and analysis for improved cancer diagnosis and treatment.
Innovation Solution
A method and system for generating cancer classification labels by acquiring and extracting primary anatomic site, histology, and invasiveness, using a lookup table to map these features, and optionally incorporating HPV status, HR level, and histopathology grade, to create a robust and reliable cancer classifier for improved data analysis and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional cancer classification systems (ICD-O, SNOMED-CT) are used, then comprehensive cancer information is available, but the systems cannot effectively support computational needs for clinical decision support systems and struggle to incorporate new genomic alterations
Solution Approach 1:
The patent segments the cancer classification into distinct modular components: anatomic site, histology, and genomic alterations. Each component is independently coded and can be updated separately, allowing new genomic alterations to be incorporated without restructuring the entire classification system. This modular approach enables the system to adapt to new discoveries while maintaining overall system stability.
Solution Approach 2:
The patent introduces a lookup table as an intermediary layer between traditional classification codes and computational decision support systems. This lookup table maps standardized anatomic site and histology codes to computationally useful features, enabling seamless integration with clinical decision support systems without modifying the original classification structures.
2Productivity
If traditional cancer classification systems are used, then established cancer categories are maintained, but the systems are slowly iterative and take years to adopt newer entities and rare tumors
Solution Approach 1:
The patent implements a dynamic classification structure where the lookup table can be updated in real-time as new tumor entities and genomic alterations are discovered. Unlike static traditional systems that require formal revision cycles, this dynamic approach allows immediate incorporation of new entities while maintaining the stable core classification framework, enabling rapid adaptation without compromising system reliability.
Solution Approach 2:
The patent performs preliminary organization of cancer data into standardized anatomic site and histology categories before integrating genomic alterations. This preliminary structuring creates a stable foundation that can rapidly accommodate new tumor entities by simply adding new combinations of existing categories, rather than requiring complete system redesign.
3Loss of information
If multiple diverse cancer classification systems are used, then abundant information is available for cancer management, but data integration and analysis become inefficient
Solution Approach 1:
The patent creates a universal lookup table structure that can interface with multiple different cancer classification systems (ICD-O, SNOMED-CT, and others). This universal interface standardizes data representation across diverse sources, enabling efficient integration and analysis without requiring separate processing pipelines for each classification system, thus reducing overall system complexity.
Solution Approach 2:
The patent creates a simplified copy or representation of cancer classification data in a computationally optimized format through the lookup table. This copy contains only the essential features needed for clinical decision support, eliminating redundant information and reducing complexity while preserving all critical diagnostic and prognostic information.
Data Source
AI summary
Systems and methods for mapping cancer classification labels are provided. In a method for generating a cancer label for a cancer patient, the method entails acquiring, from a data source, a cancer classification for a cancer in the cancer patient; extracting, from the cancer classification, primary anatomic site, primary histology, and invasiveness (or behavior) of the cancer; identifying, in a lookup table, an entry having the extracted anatomic site, histology, and invasiveness (or behavior); and retrieving a corresponding cancer label in the entry for the cancer.