Multi-Label Cancer Classification via RNA and Pathology Data Fusion
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Solution Overview
Problem
Current methods for classifying cancer, particularly those of unknown origin, face challenges due to incomplete, inconsistent, and inaccurate data in pathology reports, leading to unreliable tumor origin determination and limited access to personalized therapies.
Innovation Solution
The use of multi-type data integration, including RNA expression data, somatic genomic sequencing, and pathology reports, to improve cancer classification through iterative refinement of classification models and adaptable classifier ensembles, which address issues of incomplete and inaccurate training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pathology reports are used for cancer classification, then the process is simple and quick, but the accuracy and reliability of tumor origin determination deteriorates due to incomplete and inconsistent data
Solution Approach 1:
The patent combines multiple data sources including RNA sequencing data, pathology reports, and clinical information into an integrated classification system. This merging of diverse data types enables more accurate cancer classification by compensating for the limitations of individual data sources, directly addressing the contradiction between accuracy and complexity.
Solution Approach 2:
The classification system is designed to process multiple types of input data (RNA sequences, pathology reports, clinical information) through a unified multi-label classification framework. This multi-functional approach allows the system to handle various data formats and sources while maintaining high classification accuracy across different cancer types.
2Reliability
If multi-type data integration is implemented to improve classification accuracy, then the reliability of tumor origin determination improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the classification process into distinct components: RNA sequence analysis, pathology report processing, clinical information integration, and multi-label classification. This segmentation allows each component to be optimized independently while maintaining overall system reliability, managing the complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate processing steps including feature extraction from RNA sequences, data normalization across different input types, and integration layers that harmonize multiple data sources before final classification. These intermediaries facilitate reliable tumor origin determination by preparing and reconciling diverse data formats.
3Adaptability or versatility
If broad molecular tumor profiling is performed to enable personalized therapy selection, then the access to targeted therapies improves, but the cost and time for testing increases
Solution Approach 1:
The patent performs preliminary classification of tumor origin and type using the integrated multi-data system before proceeding to specific therapy matching. This preliminary action streamlines the overall process by quickly identifying the most relevant therapy options based on accurate tumor characterization, reducing the time needed for comprehensive molecular profiling.
Solution Approach 2:
The classification system applies different analysis depths and methods tailored to each data type and cancer category. Rather than uniformly processing all data at maximum detail, the system adjusts the level of analysis locally based on data quality, relevance, and computational efficiency requirements, optimizing both speed and accuracy.
Data Source
AI summary
Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.