CT Body Composition Segmentation for Therapy Response Prediction
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Solution Overview
Problem
Conventional predictive modeling techniques for therapeutic agent response rely on standardized body composition indices derived from a single 2D CT slice, leading to low predictive accuracy and inefficiency, as they do not account for structural components in 3D CT scans, resulting in inaccurate treatment predictions.
Innovation Solution
A preprocessing stage generates volumetric segmentation (VS) masks from 3D CT scans, forming 4D images that are used to train deep learning models for predicting therapeutic agent responses, improving training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional predictive modeling techniques use standardized body composition indices from a single 2D CT slice, then the method is simple and fast, but the predictive accuracy is low
Solution Approach 1:
The patent transitions from 2D CT slice analysis to 3D volumetric analysis by generating VS masks that capture the entire volumetric structure of body composition. This dimensional expansion allows the model to utilize spatial relationships and structural information across multiple slices, significantly improving predictive accuracy while the automated preprocessing manages the increased complexity
Solution Approach 2:
The patent implements a preprocessing stage that generates volumetric segmentation masks before the predictive modeling step. This preliminary action organizes the 3D CT data into structured VS masks, making the subsequent modeling process more efficient and manageable despite the increased data dimensionality
2Measurement precision
If conventional techniques rely on a single 2D CT slice, then the processing is efficient, but the predictive accuracy is low due to lack of structural information
Solution Approach 1:
The patent extracts only the essential body composition structures from the 3D CT scan by generating VS masks that segment and isolate relevant anatomical regions. This extraction process maintains the volumetric information needed for accurate prediction while eliminating unnecessary data, thus improving accuracy without proportionally increasing data volume
Solution Approach 2:
The patent segments the 3D CT scan into volumetric components by creating VS masks that divide the body composition into distinct anatomical structures. This segmentation organizes the data into meaningful units that can be processed efficiently while capturing the structural information necessary for accurate prediction
Data Source
AI summary
A system and method of predictive modeling of therapeutic agent response based on body composition anatomical segmentation of a computer tomography (CT) scan.The method includes acquiring a single CT scan of one or more regions of a patient. The method includes segmenting the single CT scan to generate a volumetric segmentation (VS) mask indicative of a body composition anatomical segmentation of the patient. The method includes providing the VS mask to one or more predictive models trained to predict therapeutic agent responses based on the VS mask. The method includes generating, by a processing device, a predicted treatment response score to a treatment for the patient based on the VS mask and the one or more predictive models.