Deep Image-to-Image Network for Pulmonary Nodule Malignancy Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining lung nodule malignancy in CT imaging rely on limited features, leading to unnecessary biopsies and high medical costs, as they fail to utilize the full information available in medical images.
Innovation Solution
A deep image-to-image network, including a deep reasoner network, is trained using histopathological and radiologist examination results to predict nodule malignancy by analyzing medical images, allowing for the classification or scoring of nodules and reducing the need for unnecessary biopsies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple features of CT imaging are used to determine biopsy necessity, then the decision process is simple and fast, but a significant amount of information remains unused leading to unnecessary biopsies
Solution Approach 1:
A deep learning-based radiomics system serves as an intermediary between CT imaging and biopsy decision-making. The system extracts high-dimensional radiomic features from CT images and integrates them with clinical data, then provides a comprehensive malignancy probability assessment that guides biopsy decisions, thereby utilizing previously unused imaging information without complicating the clinical workflow
Solution Approach 2:
The patent replaces the traditional manual review of simple CT features by radiologists with an automated deep learning-based radiomics analysis system. This substitution enables comprehensive extraction and analysis of complex imaging features that would be impossible for human reviewers to systematically evaluate, thereby reducing information loss while maintaining ease of operation through automated decision support
2Measurement precision
If biopsies are performed on all patients with pulmonary nodules, then malignancy can be accurately determined, but medical costs increase and patients undergo unnecessary procedures
Solution Approach 1:
The radiomics system performs preliminary malignancy assessment by analyzing CT imaging features and calculating malignancy probabilities before biopsies are performed. This preliminary action identifies low-risk patients who can be safely managed without biopsy, thereby maintaining accurate malignancy determination for high-risk patients while reducing unnecessary procedures and associated costs
Solution Approach 2:
The system transforms the approach to malignancy determination by changing from direct histopathological examination for all patients to a two-stage process: first using radiomic feature extraction and machine learning-based probability calculation, then using biopsy only when the calculated malignancy probability exceeds a threshold. This parameter change in the diagnostic workflow maintains precision while reducing resource consumption
3Measurement precision
If deep learning models are trained with multiple data sources, then prediction accuracy improves, but training complexity and data requirements increase
Solution Approach 1:
The deep learning model is designed with multi-functionality to handle multiple data types (radiomic features, clinical data, imaging data) and perform multiple tasks (feature extraction, malignancy classification, risk stratification). This universal architecture consolidates multiple processing functions into a single integrated system, improving prediction accuracy while managing training complexity through unified model design rather than separate specialized models
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
Systems and method are described for determining a malignancy of a nodule. A medical image of a nodule of a patient is received. A patch surrounding the nodule is identified in the medical image. A malignancy of the nodule in the patch is predicted using a trained deep image-to-image network.