Cancer Treatment Data System Using Segmented Genomic Processing
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Solution Overview
Problem
Current cancer treatment planning faces challenges such as variability in patient responses to treatments, limited integration of new data and insights, and inefficiencies in genomic data utilization, leading to suboptimal treatment plans and delayed adaptation to new research findings.
Innovation Solution
A system that efficiently captures and structures treatment-relevant data, including genomic information, to optimize treatment planning, integrate new insights, and provide adaptable interfaces for various user types, facilitating personalized treatment decisions and clinical trial matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive genomic data collection and analysis is implemented, then treatment accuracy and personalization improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments genomic data processing into distinct modules: data collection from multiple sources, quality control filtering, variant calling, annotation, and interpretation. Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between raw genomic data and treatment recommendations. These intermediaries include standardized data formats, quality metrics, and interpretation frameworks that simplify the integration of complex genomic information into actionable treatment plans.
2Loss of time
If real-time data integration and analysis is performed, then treatment planning timeliness improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary data preprocessing, quality control, and variant filtering during data collection and initial processing stages. By preparing data in advance with standardized formats and pre-computed metrics, the system reduces computational burden during critical treatment planning phases, enabling faster decision-making without sacrificing analysis depth.
3Loss of information
If multiple data sources and formats are integrated, then data comprehensiveness improves, but data processing complexity and standardization challenges increase
Solution Approach 1:
The patent implements a universal data integration framework that accepts multiple data sources (genomic sequencing, electronic health records, clinical trial data) and formats through standardized interfaces. This multi-functional approach allows the system to process diverse data types uniformly, maintaining information completeness while simplifying integration through consistent data structures and processing pipelines.
4Measurement precision
If advanced genomic analysis and interpretation tools are deployed, then treatment personalization improves, but ease of operation and user accessibility decrease
Solution Approach 1:
The system incorporates automated interpretation algorithms and decision support tools that independently analyze genomic data and generate treatment recommendations without requiring extensive manual intervention. The automated tools perform variant interpretation, prioritize actionable findings, and present results in clinically relevant formats, making advanced genomic analysis accessible to users without specialized bioinformatics expertise.
Data Source
AI summary
A method and system comprising storing a set of user application programs each requiring an application specific subset of data to perform application processes and generate a respective genomic variant characterization for each of a plurality of patients with cancerous cells and receiving cancer treatment. The method including, obtaining clinical records data including cancer related information, generating genomic sequencing data for the patient's cancerous cells and normal cells, shaping at least a subset of the genomic sequencing data to generate system structured data. Storing the system structured data in a first database, selecting the application specific data from the first database, storing the application specific data in a second database for application program interfacing, receiving the respective genomic variant characterization from the user application program, and storing the genomic variant characterization from the user application program in a third database.