Radiomics Analysis Guidance for Accurate Study Design
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Solution Overview
Problem
Current radiomics analyses face challenges due to the complexity of statistical and machine learning expertise requirements, leading to potential misinterpretation of data associations, lack of correct study design, and inefficient publication processes, especially when integrating multispectral imaging and other high-dimensional 'omics' data, which hampers the scientific quality and dissemination of novel technologies.
Innovation Solution
A computer-implemented method and system that provides radiomics-related information through an assistance algorithm, guiding users in data processing and analysis, including automated statistical analysis, visualization, and publication preparation, leveraging machine learning and expert knowledge to ensure accurate and efficient study execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiomics analysis is performed with comprehensive statistical and machine learning methods, then the scientific quality and accuracy of results improve, but the complexity of expertise requirements and device complexity increase
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between raw radiomics data and scientific conclusions. This system embeds statistical and machine learning expertise within automated algorithms, allowing users without advanced statistical knowledge to perform high-quality radiomics analysis. The system serves as a mediator that translates complex statistical methods into user-friendly tools, resolving the contradiction between maintaining scientific quality and reducing expertise requirements.
2Measurement precision
If comprehensive statistical analysis methods are applied to radiomics data, then the accuracy of identifying meaningful connections improves, but the time and resources required for analysis increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring statistical analysis pipelines, pre-selecting appropriate statistical methods based on data characteristics, and pre-establishing quality control parameters. The system automatically performs data preprocessing, selects suitable statistical tests, and applies appropriate correction methods before the actual analysis begins. This preparation work is done automatically in the background, allowing users to obtain accurate results without manually spending time on each statistical decision.
Solution Approach 2:
The automated analysis system performs self-service by automatically selecting and applying appropriate statistical methods based on the characteristics of the input data. The system autonomously determines sample size requirements, selects statistical tests, applies multiple comparison corrections, and generates quality control reports without requiring user intervention in each statistical decision. This self-service capability maintains high analytical accuracy while significantly reducing the time and expertise burden on users.
3Adaptability or versatility
If radiomics analysis integrates multiple high-dimensional 'omics' data, then the comprehensiveness of clinical decision support improves, but the statistical complexity and difficulty of correct analysis increase
Solution Approach 1:
The patent implements universality by designing a multi-functional analysis platform that can handle various types of radiomics data, imaging modalities, and other omics data types within a single integrated system. The platform provides universal statistical methods and machine learning algorithms that work across different data types and analysis scenarios. This multi-functional approach allows comprehensive integration of multiple high-dimensional data sources while managing statistical complexity through standardized processing pipelines and unified analysis frameworks.
Data Source
AI summary
A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.