Lymphocyte-Tumor Interaction Score for Therapy Prediction
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Solution Overview
Problem
Current cancer therapies, such as immunotherapy and targeted therapy, often have variable effectiveness and may not guarantee therapeutic success for all cancer patients, necessitating a method to predict responsiveness to therapy before treatment.
Innovation Solution
A system and method that utilize pathology slide images to calculate a lymphocyte and tumor cell interaction score (LTS) by determining the positions and characteristics of lymphocytes and tumor cells, and using this score in a machine learning model to predict therapy responsiveness, incorporating clinical factors and normalizing histological components for consistency across different medical systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cancer therapy is administered to all patients, then treatment coverage is improved, but therapeutic effectiveness deteriorates due to variable responsiveness
Solution Approach 1:
The patent performs preliminary analysis of pathology slide images to calculate lymphocyte-tumor cell interaction scores and predict therapy responsiveness before administering treatment. This preliminary action enables selection of patients most likely to respond to immunotherapy, thereby improving overall therapeutic effectiveness while avoiding unnecessary treatments for non-responders.
2Measurement precision
If manual pathology analysis is performed, then measurement precision is improved, but productivity deteriorates due to time-consuming evaluation
Solution Approach 1:
The patent replaces manual mechanical pathology review with an automated computer-based system that processes pathology slide images through machine learning algorithms. This substitution maintains measurement precision in assessing lymphocyte-tumor cell interactions while dramatically increasing productivity by enabling simultaneous evaluation of multiple patients' images.
Solution Approach 2:
The system creates digital copies of pathology slide images for automated analysis, allowing multiple evaluations of the same sample without consuming additional biological materials. These digital copies can be processed repeatedly through the machine learning model to generate consistent interaction scores and predictions.
3Quantity of substance
If pathology slide images from multiple hospitals are used, then data quantity is improved, but measurement precision deteriorates due to inconsistencies across medical systems
Solution Approach 1:
The patent applies parameter changes by normalizing histological component measurements across different pathology slide images from various hospitals. The system adjusts for variations in staining protocols, imaging equipment, and preparation methods by standardizing the extraction of lymphocyte and tumor cell characteristics, thereby maintaining measurement precision while utilizing large diverse datasets.
Data Source
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AI summary
A method for predicting responsiveness to therapy for cancer patient is provided, which includes acquiring a pathology slide image of a cancer patient, determining information on a plurality of lymphocytes and information on a plurality of tumor cells included in the pathology slide image, calculating a lymphocyte and tumor cell interaction score based on the information on the plurality of lymphocytes and the information on the plurality of tumor cells, and predicting responsiveness to therapy for the cancer patient by using the interaction score.