Virtual Staging Score for Biopsy-Free Cancer Detection
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Solution Overview
Problem
Current methods for detecting cancer, such as prostate cancer, face challenges including high false positive rates in PSA tests, difficulties in obtaining uniform tissue samples during biopsies, and reliance on invasive procedures that can cause complications, as well as inconclusive results due to sampling location issues, which are also applicable to other types of cancer like breast and lung cancer.
Innovation Solution
A biopsy-free method using a virtual staging score calculated from medical image data, including anatomical and functional imaging modalities like MRI and PET, combined with patient demographic and blood test data, to predict cancer staging scores non-invasively, eliminating the need for biopsies and potentially reducing unnecessary surgeries and complications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a biopsy is performed to detect cancer, then cancer detection can be performed, but the procedure is invasive and can cause bleeding, hemorrhage, and unnecessary complications
Solution Approach 1:
The patent creates a virtual copy of the biopsy process by using MRI imaging to generate a virtual Gleason score that replicates the diagnostic information normally obtained from tissue sampling. This virtual score model predicts cancer aggressiveness and staging without requiring actual tissue removal, thereby eliminating the harmful invasive effects while maintaining diagnostic reliability.
Solution Approach 2:
The patent replaces the mechanical biopsy procedure with an imaging-based diagnostic system. Instead of physically removing tissue through a needle or scope, the system uses MRI scans to non-invasively capture cancer characteristics and compute a virtual Gleason score, substituting mechanical tissue sampling with electromagnetic imaging and computational analysis.
2Productivity
If a PSA blood test is used for initial detection, then cancer screening can be performed, but the test has a very high false positive rate leading to unnecessary biopsies
Solution Approach 1:
The patent implements a feedback mechanism where the virtual Gleason score is calculated based on MRI imaging results and patient characteristics, providing a more accurate assessment that feeds back into the diagnostic decision-making process. This feedback loop allows clinicians to distinguish between true positives and false positives more effectively, reducing unnecessary biopsies while maintaining screening productivity.
Solution Approach 2:
The patent changes the diagnostic parameter from PSA blood test results to MRI-based virtual Gleason scores. This parameter transformation enables more precise cancer characterization by using imaging data that directly visualizes tumor morphology and characteristics, thereby improving reliability and reducing false positives while maintaining efficient screening capabilities.
3Measurement precision
If a biopsy is performed to obtain tissue samples, then cancer diagnosis can be made, but it is challenging to acquire samples from all areas of the prostate, especially the peripheral zone
Solution Approach 1:
The patent creates a comprehensive virtual map of the prostate using MRI imaging that replicates the complete tissue architecture without requiring physical sampling. The virtual Gleason score model processes this imaging data to assess cancer characteristics across the entire prostate volume, including difficult-to-reach peripheral zones, thereby achieving measurement precision equivalent to complete biopsies without the operational difficulties of performing such biopsies.
4Reliability
If Gleason scoring is performed based on tissue location, then cancer staging can be determined, but results are inconclusive when tissue is sampled from the wrong location
Solution Approach 1:
The patent creates a complete virtual replica of the prostate anatomy and cancer lesions through MRI imaging, eliminating the information loss that occurs when biopsies miss the correct location. The virtual Gleason score model processes this comprehensive imaging data to accurately determine cancer staging and aggressiveness, ensuring reliable results without the inconclusiveness that arises from sampling errors in traditional biopsies.
Data Source
AI summary
A method for predicting a cancer staging score from medical image data includes receiving patient data for a plurality of patients, where patient data for each of the plurality of patients includes one or more of an image volume of a suspected tumor in an organ, blood test data, demographic data, and ground truth tumor staging scores for the suspected tumor in the organ, extracting features from the patient data, and using the features extracted from the patient data to train a classifier to predict a cancer staging score for a new patient from one or more of an image volume of a suspected tumor in the organ, patient blood test data and patient demographic data of that new patient.