Tumor Morphology Analysis for Melanoma Recurrence Prediction
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Solution Overview
Problem
Current methods for evaluating the risk of recurrence in early-stage melanoma are inadequate, as they are invasive, costly, and prone to over-treatment, and existing AI applications in pathology are complex and not widely applicable in clinical settings.
Innovation Solution
A method using deep learning techniques, specifically convolutional neural networks, analyzes images of tissue samples to classify cells and calculate morphological features, predicting the probability of distant metastatic recurrence through image segmentation and feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the AJCC staging system is used to evaluate risk of recurrence, then comprehensive tumor characterization is achieved, but the process becomes invasive and costly
Solution Approach 1:
The patent extracts and emphasizes only the most critical morphological features (necrosis, mitotic rate, tumor-infiltrating lymphocytes) from the comprehensive AJCC staging parameters, creating a simplified prognostic model that maintains predictive accuracy while eliminating unnecessary invasive procedures and cost
Solution Approach 2:
The patent uses digital image analysis to create a virtual copy of the tissue sample morphology, allowing comprehensive characterization without requiring physical manipulation or invasive procedures on the actual tissue specimen
2Reliability
If adjuvant therapy is administered to all early stage melanoma patients, then recurrence prevention is maximized, but over-treatment and resource expenditure increase significantly
Solution Approach 1:
The patent applies local quality by providing personalized prognostic assessment based on individual tumor morphology rather than uniform treatment protocols, allowing high-risk patients to receive intensive therapy while low-risk patients receive conservative management
Solution Approach 2:
The patent changes the decision parameter from blanket staging categories to continuous morphological feature measurements, enabling more precise risk stratification and optimized treatment allocation
3Measurement precision
If deep learning methods are applied to pathology imaging, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent extracts and focuses on a limited set of key morphological features rather than attempting to analyze all possible image characteristics, simplifying the deep learning model while maintaining high predictive accuracy
Solution Approach 2:
The patent introduces trained experts as intermediaries who validate and interpret the deep learning predictions, bridging the gap between complex algorithmic output and clinically actionable insights
Data Source
AI summary
A method of treating a subject comprises administering a treatment to a subject identified as having a high probability of distant metastatic recurrence, wherein the probability of distant metastatic recurrence was determined by a process, comprising acquiring at least one image of a tissue sample comprising a plurality of cells, taken from a subject, classifying each of the plurality of cells into categories, dividing the at least one image into a plurality of patches, calculating values for a plurality of morphological features based on the patches, and calculating a distant metastatic recurrence probability based on the values. A computer-implemented method of training a neural network and a system for characterizing a cancer in a subject are also described.