Multimodal Tumor Imaging Analysis for GBM Heterogeneity
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Solution Overview
Problem
Current diagnostic methods for GBM (glioblastoma) fail to adequately assess tumor heterogeneity due to the risks of invasive biopsies and limitations of non-invasive techniques like RANO, which cannot account for tumor traits during treatment.
Innovation Solution
A multimodal analysis system using machine learning and deep learning to segment tumors from medical images, perform volumetric and radiomic analyses, and predict clinical outcomes based on imaging and historical data, enabling non-invasive assessment of GBM heterogeneity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive biopsy is performed to assess GBM heterogeneity, then measurement precision is improved, but object-affected harmful factors increase due to risks associated with brain biopsies
Solution Approach 1:
The patent introduces MRI imaging as an intermediary non-invasive technique to assess tumor heterogeneity. Instead of directly sampling tissue through biopsy, the system uses magnetic resonance imaging to obtain indirect information about tumor characteristics, thereby avoiding the harmful effects of invasive procedures while still enabling heterogeneity assessment
Solution Approach 2:
The patent replaces the mechanical invasive biopsy procedure with a non-invasive imaging-based assessment system. By substituting physical tissue sampling with MRI-based radiomic analysis, the system eliminates the need for mechanical intrusion into the brain while maintaining the ability to evaluate tumor heterogeneity
2Object-affected harmful factors
If non-invasive RANO technique is used to assess treatment response, then object-affected harmful factors are reduced, but measurement precision deteriorates as RANO cannot account for tumor heterogeneity or emerging traits
Solution Approach 1:
The patent transforms the assessment parameters by extracting multiple radiomic features from MRI images, including tumor intensity, shape, and texture characteristics. This multi-parameter approach enables comprehensive evaluation of tumor heterogeneity and treatment response without requiring invasive procedures
Solution Approach 2:
The patent creates a multi-functional assessment system that simultaneously evaluates tumor heterogeneity, treatment response, and emerging traits during therapy. The radiomic analysis framework integrates multiple assessment capabilities into a single non-invasive platform, overcoming the limitations of specialized single-purpose techniques like RANO
3Measurement precision
If whole-tumor biopsy is performed to assess GBM heterogeneity, then measurement precision is improved, but device complexity and procedure difficulty increase
Solution Approach 1:
The patent applies segmentation by dividing the complex task of tumor assessment into distinct radiomic feature categories (intensity, shape, texture). This segmentation allows each aspect of tumor heterogeneity to be evaluated independently through non-invasive imaging, avoiding the need for complex whole-tumor biopsy procedures
Data Source
AI summary
Systems and methods for predicting clinical outcomes of a patient are provided. An input medical image of a tumor of a patient is received. The tumor is segmented from the input medical image. One or more assessments of the tumor are performed based on the segmentation. A clinical outcome of the patient is predicted based on results of the one or more assessments of the tumor. The clinical outcome of the patient is output.