Quantitative Tumor Vasculature Analysis for MRI Outcome Prediction
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Solution Overview
Problem
Current methods for analyzing tumor-associated vasculature on dynamic contrast enhanced MRI provide indirect characterization of vascularization, failing to capture the direct computational analysis of tumor-associated vessel network morphology and function, which is crucial for predicting therapeutic response and prognosis.
Innovation Solution
A computational approach using quantitative imaging features to analyze the morphology, spatial organization, and function of tumor-associated vasculature, employing machine learning models to predict neoadjuvant therapy response and disease prognosis based on features such as vessel shape and enhancement profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indirect quantitative analysis of tumors on DCE MRI is used, then vascularization can be characterized, but direct computational analysis of tumor-associated vessel network morphology and function is not captured
Solution Approach 1:
The patent extracts and isolates tumor-associated vessel networks from DCE MRI data through computational segmentation and analysis. This extraction process separates the vessel network information from the indirect tumor signal, enabling direct computational analysis of vessel morphology, spatial organization, and functional characteristics that were previously lost in conventional indirect analysis methods
Solution Approach 2:
The patent introduces computational image processing algorithms and machine learning models as intermediaries between the DCE MRI data and the final vascular characterization. These computational tools serve as mediators that transform indirect tumor enhancement signals into direct vessel network measurements, preserving morphological and functional information that bridges the gap between indirect imaging and direct vascular analysis
2Reliability
If conventional DCE MRI analysis is used, then tumor vascularization can be assessed, but predictive accuracy for therapeutic response is insufficient
Solution Approach 1:
The patent transforms conventional DCE MRI analysis by introducing new quantitative parameters that directly characterize vessel network properties. These include vessel density, tortuosity, branching patterns, spatial organization metrics, and functional flow characteristics. By changing the measured parameters from indirect tumor enhancement to direct vessel network properties, the patent achieves both improved measurement precision and enhanced predictive accuracy for therapeutic response
Solution Approach 2:
The patent replaces conventional visual and semi-quantitative assessment methods with computational image processing and machine learning algorithms. This substitution transforms the analysis from indirect tumor-based metrics to direct vessel network characterization, enabling precise measurement of morphological and functional parameters that significantly improve therapeutic response prediction reliability
3Measurement precision
If direct computational analysis of vessel network is implemented, then predictive accuracy improves, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex computational analysis into distinct modular stages: initial vessel network segmentation from DCE MRI data, extraction of morphological features (vessel density, tortuosity, branching), calculation of spatial organization metrics, and computation of functional characteristics. This segmentation of the computational process reduces overall complexity by breaking down the complex analysis into manageable, standardized modules that can be processed systematically
Solution Approach 2:
The patent performs preliminary processing of DCE MRI data through standardized preprocessing steps including noise reduction, contrast enhancement optimization, and initial vessel segmentation before detailed morphological and functional analysis. By preparing the data in advance with preliminary actions, the subsequent complex computational analysis becomes more efficient and manageable, reducing the overall computational burden while maintaining high measurement precision
Data Source
AI summary
The present disclosure relates to a method. The method may be performed by accessing data derived from one or more routine clinical medical imaging scans including a lesion in which the lesion and associated vasculature are segmented in a three-dimensional segmentation. At least two features are extracted from the three-dimensional segmentation of the associated vasculature. The at least two features include at least one feature indicative of a morphology of the associated vasculature or a portion thereof, and at least one feature indicative of a function of the associated vasculature or a portion thereof. The at least two features, and/or one or more statistics of the at least two features, are provided to a machine learning model trained to make a prediction concerning the lesion. The prediction concerning the lesion is received from the machine learning model.