Deep Learning PET CT Lesion Quantification for Prostate Cancer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting biochemical progression-free survival (bPFS) in prostate cancer patients are inadequate, as they lack accurate and efficient tools for analyzing medical images to detect and quantify cancer lesions effectively.
Innovation Solution
The use of deep learning techniques, specifically Convolutional Neural Networks (CNNs), to segment anatomical information from CT images and combine it with PET images to detect and quantify prostate cancer lesions, thereby determining patient risk indices such as SUVmean, SUVmax, PSMA positive total tumor volume, and aPSMA scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used for predicting bPFS, then the process is simple, but the accuracy and reliability of lesion detection and quantification are inadequate
Solution Approach 1:
The patent introduces deep learning models as intermediary systems between the PET/CT images and the bPFS prediction outcome. These models act as mediators that automatically segment anatomical structures, detect lesions, and quantify tumor burden, thereby improving measurement precision without requiring manual intervention while managing system complexity through automated processing pipelines
Solution Approach 2:
The patent replaces manual image analysis methods with automated deep learning-based image processing. Convolutional neural networks substitute for human radiologists in segmenting anatomical information from CT images and detecting lesions in PET images, significantly improving measurement precision and consistency while reducing the complexity of manual操作流程
2Productivity
If manual image analysis is used, then the system complexity is low, but the productivity and efficiency of bPFS prediction are insufficient
Solution Approach 1:
The patent implements self-service through automated deep learning models that independently perform image segmentation, lesion detection, and quantification without requiring manual intervention. The system processes PET/CT images autonomously to generate bPFS predictions, dramatically improving productivity while the modular architecture manages complexity by separating processing stages
Solution Approach 2:
The patent applies preliminary action by pre-training deep learning models on large datasets of annotated PET/CT images before deployment. This preliminary training enables the models to automatically perform complex image analysis tasks with high accuracy, improving productivity while the pre-processed knowledge reduces the complexity of real-time decision-making during actual bPFS prediction
3Measurement precision
If automated deep learning methods are implemented, then lesion quantification accuracy improves, but the device and processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image analysis process into distinct modular stages: anatomical structure segmentation from CT images, lesion detection in PET images, and tumor burden quantification. This segmentation improves measurement precision for each specific task while managing overall system complexity through standardized, reusable processing modules
Solution Approach 2:
The patent implements universality through deep learning models that perform multiple functions within a single integrated system. The same architectural framework handles anatomical segmentation, lesion detection, and quantification across different PET/CT datasets, improving quantification accuracy while reducing complexity by avoiding the need for separate specialized systems for each task
Data Source
AI summary
Presented herein are systems and methods for predicting biochemical progression free survival (bPFS) in prostate cancer patients. In certain embodiments, bPFS is predicted from 18F-DCFPyL PET/CT images using deep learning (or other machine learning or artificial intelligence techniques) to segment anatomical information from the CT image and use this information in combination with the PET image to detect and quantify candidates for prostate cancer lesions.