Glandular Tissue Grading via Digital Shape Index Analysis
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Solution Overview
Problem
The conventional Gleason method for prostate cancer grading is prone to inconsistencies and missed areas due to manual evaluation of stained tissue, which affects prognostic accuracy and is not effective in predicting cancer recurrence after radical prostatectomy.
Innovation Solution
A novel method that uses digital image analysis to identify and quantify geometric features of individual glands in prostate tissue, calculating a shape index to predict cancer malignancy and recurrence based on geometric characteristics without relying on tissue patterns, thereby improving the accuracy of cancer grading and predicting biochemical recurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual Gleason grading is performed by pathologists, then cancer tissue can be evaluated and graded, but inconsistencies and missed areas occur leading to reduced prognostic accuracy
Solution Approach 1:
The patent replaces the manual mechanical evaluation system (pathologist visual inspection and scoring) with an automated digital image analysis system. The system uses computer algorithms to process digital images of stained tissue sections, automatically identify glands, calculate shape indices, and generate Gleason scores without human intervention, thereby eliminating inconsistencies and missed areas inherent in manual grading.
Solution Approach 2:
The digital image analysis system performs self-evaluation by automatically processing tissue images through a standardized algorithm. The system independently identifies glands, measures their geometric features, calculates shape indices, and determines Gleason scores without requiring external human judgment, ensuring consistent and reproducible results across different samples and operators.
2Productivity
If pathologists manually evaluate magnified images of stained tissue, then Gleason scores can be assigned, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces the slow manual evaluation process with an automated digital image analysis system that rapidly processes tissue images. The system uses computer algorithms to automatically identify glands, measure geometric features, calculate shape indices, and assign Gleason scores in a fraction of the time required for manual evaluation, while maintaining or improving grading accuracy through consistent application of objective criteria.
Solution Approach 2:
The digital image analysis system enables continuous automated processing of tissue images without the interruptions, fatigue, or variability inherent in manual evaluation. The system can process multiple images sequentially without loss of focus or attention, maintaining constant productivity and precision throughout the grading process.
3Measurement precision
If Gleason grading relies on pattern evaluation of multiple glands, then cancer severity can be assessed, but subjective interpretation reduces prognostic accuracy
Solution Approach 1:
The patent extracts the essential diagnostic information from complex tissue patterns by focusing specifically on the geometric shape of individual glands. Instead of evaluating overall tissue architecture and patterns of multiple glands, the system measures simple geometric features (area, perimeter, shape index) of each gland and uses these extracted features to determine Gleason scores, thereby simplifying the evaluation while improving objectivity and precision.
Solution Approach 2:
The patent transforms the complex subjective assessment of tissue patterns into objective quantitative measurements of gland geometry. By changing the evaluation parameters from qualitative pattern recognition to quantitative shape index calculations, the system eliminates subjective interpretation and provides precise, reproducible prognostic assessment based on measurable geometric characteristics.
Data Source
AI summary
An improved histopathological score is obtained by identifying objects in images of glandular tissue from cancer patients. The objects are identified based on staining by a biomarker. The score predicts that a cancer patient will have a recurrence of cancer of the glandular tissue based on a geometric characteristic of individual identified objects but not on any pattern formed by the identified objects. First objects are generated from the image of glandular tissue which has been stained with a single biomarker that stains epithelial cells. Second objects are then generated using the first objects. A geometric feature of each of the second objects is measured. A shape index is then calculated for each of the second objects based on the geometric feature, and an average shape index is calculated. Based on the average shape index, a score is determined that indicates a level of cancer malignancy of the glandular tissue.