Neoplasm Phenotype Determination via Image Feature Signature Analysis
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Solution Overview
Problem
Current imaging technologies for neoplasms, such as tumors, rely heavily on subjective interpretations and lack objective methods for prognostication and treatment selection, as they fail to capture the spatial and temporal heterogeneity of tumors effectively, limiting their ability to provide personalized medicine approaches.
Innovation Solution
An image analysis method that uses a processing unit to derive neoplasm signature model values from image feature parameters, including gray-level non-uniformity, wavelet high-low-high gray-level run-length gray-level non-uniformity, statistics energy, and shape compactness, to aid in prognostication and treatment selection by converting imaging data into a high-dimensional feature space for predictive and prognostic analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging methods are used to assess tumor characteristics, then the imaging process is simple and non-invasive, but the interpretation remains subjective and fails to capture spatial and temporal heterogeneity effectively
Solution Approach 1:
The patent replaces subjective human interpretation with automated computational analysis. A processing unit extracts quantitative image feature parameters from medical images and applies a signature model with functional relations to derive objective neoplasm phenotype values, eliminating observer bias while capturing tumor heterogeneity through systematic analysis of multiple image features
Solution Approach 2:
The patent transforms conventional single-parameter tumor assessment (e.g., size only) into multi-parameter phenotypic characterization. By extracting multiple image feature parameters (intensity, texture, shape, etc.) and combining them through signature models, the system captures spatial and temporal heterogeneity that single parameters cannot detect
2Adaptability or versatility
If only tumor size is measured as routinely done in clinical practice, then the measurement is simple and clinically validated, but it does not reflect pathological differences or enable personalized medicine
Solution Approach 1:
The patent segments the tumor characterization process into multiple independent image feature parameters (intensity, texture, shape, etc.) that can be analyzed separately and then integrated. This segmentation allows each parameter to capture specific aspects of tumor biology, enabling personalized treatment decisions based on the composite phenotypic profile
Solution Approach 2:
The signature model serves multiple functions: it integrates multiple image features, applies functional relations to derive phenotype values, and provides both diagnostic and prognostic information. This multi-functional approach enables personalized medicine without requiring separate systems for each function
3Measurement precision
If sophisticated data analysis methods are implemented to derive information from imaging data, then prognostic accuracy improves, but the ease of operation for medical practitioners decreases
Solution Approach 1:
The system performs automated feature extraction and phenotype derivation without requiring manual intervention from medical practitioners. The processing unit automatically extracts image features, applies signature models, and generates prognostic values, making the sophisticated analysis transparent and easy to use while maintaining high accuracy
Data Source
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AI summary
The present invention relates to a decision support system and an image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, comprising the steps of: receiving, by a processing unit, image data of the neoplasm; and deriving, by the processing unit, a plurality of image feature parameter values from the image data, said image parameter values relating to image features associated with the neoplasm; and deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein said signature model includes a functional relation between or characteristic values of said image feature parameter values for deriving said neoplasm signature model values