Radiotherapy System Tumor Classification
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Solution Overview
Problem
Current radiotherapy planning methods fail to accurately distinguish between primary and metastatic lesions in tumors, leading to inadequate treatment planning and potential oversight of metastatic lesions.
Innovation Solution
A radiotherapy system and therapy planning method that utilize processing circuitry to classify tumor areas as either primary or metastatic lesions using feature amounts from CT and other medical images, employing machine learning algorithms to differentiate between the two and mark them appropriately for targeted treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tumor outlining methods are used without distinction between primary and metastatic lesions, then the treatment planning process is simple and quick, but the accuracy of treatment planning deteriorates due to inadequate differentiation of lesion types
Solution Approach 1:
The patent replaces manual visual inspection and conventional outlining methods with automated image processing and machine learning algorithms. The system automatically analyzes medical images to identify and classify tumor areas as primary or metastatic lesions, substituting the mechanical/manual process with an automated intelligent system that provides accurate classification without requiring complex manual differentiation procedures
Solution Approach 2:
The system enables self-service by automatically performing tumor classification and marking without requiring expert intervention. The machine learning model autonomously processes medical images, identifies tumor characteristics, and generates treatment planning information, allowing the system to serve itself in the classification task without external assistance
2Measurement precision
If manual differentiation of primary and metastatic lesions is performed, then classification accuracy may improve, but the time and resources required for treatment planning increase significantly
Solution Approach 1:
The system performs preliminary classification of tumor areas before treatment planning begins. By pre-identifying and marking primary versus metastatic lesions in advance, the system prepares classification results that can be directly used in subsequent treatment planning steps, eliminating the need for time-consuming manual differentiation during the planning process itself
Solution Approach 2:
The patent substitutes manual expert analysis with automated machine learning algorithms that rapidly process medical images. The system uses trained models to automatically differentiate lesion types based on image features, achieving accurate classification at speeds much faster than manual methods while requiring minimal human time investment
3Reliability
If comprehensive analysis of all tumor areas is performed to identify metastatic lesions, then treatment completeness improves, but the complexity of image analysis and processing increases
Solution Approach 1:
The system segments the analysis process into distinct functional modules: tumor area detection, feature extraction, classification (primary vs. metastatic), and marking. By dividing the comprehensive analysis into these separate segments, the system can thoroughly examine all tumor areas while managing complexity through modular design, where each module handles a specific aspect of the analysis independently
Solution Approach 2:
The patent implements a universal image analysis system that handles multiple functions: detecting tumors, classifying lesion types, marking areas, and generating treatment planning information. This multi-functional system processes all tumor areas comprehensively using a single integrated platform, improving treatment completeness while avoiding the need for multiple separate complex systems
4Measurement precision
If automated classification systems are implemented to distinguish primary and metastatic lesions, then treatment accuracy improves, but the computational resources and system complexity increase
Solution Approach 1:
The patent replaces complex manual classification procedures with automated machine learning algorithms. The system uses trained models that automatically analyze image features and classify lesions, substituting the need for complex manual expert systems with streamlined automated processing that achieves high accuracy through algorithmic pattern recognition rather than complex system architecture
Solution Approach 2:
The system achieves accurate classification by analyzing changes in image parameters and features extracted from medical images. The machine learning model processes various image parameters (density, texture, shape characteristics) and uses their patterns to differentiate primary from metastatic lesions, achieving high accuracy through parameter analysis rather than system complexity
Data Source
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AI summary
A radiotherapy system according to an embodiment includes processing circuitry. The processing circuitry specifies at least one tumor area in a medical image of a patient, and outputs classification result information including a result of classification this/these tumor area(s) as either a primary lesion or a metastatic lesion based on an image feature amount of this/these tumor area (s).