Image Analysis Method for Neoplasm Treatment Prediction
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Solution Overview
Problem
Current imaging techniques for neoplasm treatment effectiveness assessment are invasive and limited in capturing temporal and spatial heterogeneity, necessitating non-invasive methods for early detection of treatment efficacy to improve patient outcomes.
Innovation Solution
An image analysis method using a prediction model that combines carefully selected image features from CT and PET imaging data, calculating difference values and multiplying them with associated multiplier values to derive a predictive value indicating treatment effectiveness, enabling early modification of treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biopsies or invasive surgeries are used to extract tissue for molecular characterization, then molecular characterization of neoplasms is enabled, but the procedure becomes invasive and requires surgical intervention
Solution Approach 1:
The patent uses imaging data as an intermediary to obtain molecular characterization information without direct tissue extraction. The image analysis method processes imaging data to derive molecular features, serving as a mediator between non-invasive imaging and molecular-level diagnosis, thereby eliminating the need for invasive biopsies while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical invasive procedure of biopsy and surgery with a non-invasive image analysis system. By substituting physical tissue extraction with computational analysis of imaging data, the system achieves molecular characterization without mechanical intrusion into the patient's body
2Ease of operation
If imaging is used to capture intra-tumoral heterogeneity non-invasively, then the procedure becomes non-invasive and repeatable, but the ability to obtain molecular characterization is limited compared to biopsy
Solution Approach 1:
The patent transforms imaging parameters into molecular characterization parameters through advanced image analysis. By changing the interpretation parameters of imaging data from anatomical/structural to molecular/biochemical, the system extracts molecular-level information (such as metabolic activity, cellular density, and tissue composition) from conventional imaging modalities, thereby achieving molecular characterization without invasive procedures
Solution Approach 2:
The patent adds a computational analysis dimension to traditional imaging. By introducing multi-parametric image analysis that extracts features beyond visual inspection (such as texture analysis, intensity distribution, and temporal changes), the system transforms 2D/3D imaging data into molecular-level insights, effectively adding an informational dimension that bridges imaging and molecular diagnosis
3Device complexity
If treatment effectiveness is assessed using conventional imaging, then the assessment is simple, but early detection of treatment effectiveness is delayed
Solution Approach 1:
The patent performs preliminary analysis of imaging parameters that are sensitive to treatment effects before clinical outcomes become apparent. By monitoring specific image features (such as early changes in tumor texture, intensity, or volume) that precede visible tumor shrinkage, the system detects treatment effectiveness in advance, enabling earlier intervention while maintaining a relatively simple assessment framework
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors imaging parameters during treatment and compares them against expected treatment responses. This real-time feedback allows for early detection of treatment effectiveness or resistance, enabling timely adjustment of treatment plans while keeping the assessment process integrated into the existing treatment workflow
Data Source
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AI summary
The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.