Digital Pathology Image Processing for Objective pCR Assessment
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Solution Overview
Problem
Current methods for determining pathological complete response (pCR) and minimal residual disease (MRD) in cancer treatment are subjective and challenging due to varying definitions and treatment effects that alter tissue morphology, leading to delays and potential errors in diagnosis.
Innovation Solution
A system and method using machine learning models, trained on digital pathology images with and without treatment effects, to automatically determine pCR and MRD by analyzing tissue specimens, providing objective and accurate cancer qualifications and quantifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination by pathologists is used to determine pCR/MRD, then diagnostic accuracy can be maintained through expert judgment, but the process is subjective and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical examination process with an automated digital image processing system. Whole slide images are processed through a computational pipeline that includes image registration, segmentation, and classification algorithms to automatically detect and quantify residual cancer cells, eliminating the need for manual microscopic examination while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces a computational intermediary layer between the tissue sample and the final diagnosis. Digital pathology images serve as an intermediary representation of the tissue, which is then processed through multiple algorithmic stages (registration, segmentation, classification) to produce quantitative results, bridging the gap between raw tissue data and clinical decision-making.
2Reliability
If manual pathologist examination is used, then diagnostic thoroughness can be achieved, but subjectivity and variability increase
Solution Approach 1:
The patent segments the diagnostic process into distinct computational modules: image registration to align multiple slides, segmentation to identify tissue regions and cell boundaries, and classification to categorize cells as cancerous or benign. This modular approach standardizes each step, reducing subjectivity while managing system complexity through structured processing.
Solution Approach 2:
The patent transforms qualitative pathological assessment into quantitative parameters. Instead of relying on pathologist subjective judgment, the system measures objective features such as cell morphology, nuclear characteristics, and tissue architecture metrics, converting diagnostic criteria into quantifiable data that can be consistently processed and compared.
3Adaptability or versatility
If treatment effects are present in tissue samples, then clinical relevance is improved, but detection difficulty increases due to morphological changes
Solution Approach 1:
The patent performs preliminary image registration and quality assessment before the main detection process. By pre-processing the images to correct for registration errors, normalize staining variations, and identify regions of interest, the system prepares the data in advance to handle treatment-induced morphological changes, making the subsequent detection more robust and accurate.
Data Source
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AI summary
Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen, determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further a cancer quantification if the cancer qualification is an confirmed cancer qualification, providing the digital image as an input to the detection machine learning model, receiving one of a pathological complete response (pCR) cancer qualification or a confirmed cancer quantification as an output from the detection machine learning model, and outputting the pCR cancer qualification or the confirmed cancer quantification.