3D Tumor Culture Imaging for T Cell Infiltration Mapping
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Solution Overview
Problem
Current methods for analyzing T cell behavior in tumors are limited, particularly in quantifying infiltration and cytotoxicity, and lack a systematic approach to model tumor progression and predict patient survival, especially in the context of immune evasion and hypoxia conditions.
Innovation Solution
A machine learning-enabled algorithm using a residual neural network with a convolutional network architecture analyzes 3D tumor tissue images to create T cell infiltration maps, scoring drugs or agents that can enhance T cell infiltration and predict patient survival by optimizing model weights through a loss function to minimize negative log likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 2D and 3D models are used to analyze T cell behavior, then T cell cytotoxicity and cytokine profiles can be obtained, but the analysis is subjective and lacks detailed information on T cell infiltration and behavior
Solution Approach 1:
The patent replaces subjective manual analysis methods with an automated machine learning system comprising a neural network and computer algorithm. This substitution transforms the analytical process from subjective interpretation of images to objective automated quantification of T cell infiltration patterns, density, and distribution in 3D tumor models, thereby improving measurement precision while managing system complexity through software automation.
2Loss of information
If intensive downstream analysis (ELISA, qRT-PCR, Flow-Cytometry) is performed to analyze T cell functionality, then detailed T cell behavior information can be obtained, but the process is time-consuming and complex
Solution Approach 1:
The patent extracts and focuses specifically on image-based T cell infiltration analysis using machine learning, separating this function from the need for intensive downstream molecular analysis. By training the neural network to directly quantify T cell behavior, density, and distribution from images alone, the system retrieves comprehensive T cell behavior information while eliminating time-consuming wet lab procedures like ELISA, qRT-PCR, and Flow-Cytometry.
Solution Approach 2:
The patent creates a computational model that copies and simulates T cell behavior analysis through image processing and machine learning algorithms. Instead of performing physical downstream analysis on biological samples, the system creates virtual representations of T cell infiltration patterns and behaviors from images, achieving comprehensive behavioral information through computational simulation rather than physical experimentation.
3Productivity
If simple readouts (T cell cytotoxicity and cytokine profiles) are used from 2D and 3D models, then the analysis is quick and simple, but detailed information on T cell infiltration and cytotoxicity is lacking
Solution Approach 1:
The patent segments the analysis into distinct quantifiable parameters including T cell infiltration density, distribution patterns, spatial organization, and behavioral characteristics. The machine learning system processes images to extract multiple independent features simultaneously, providing comprehensive detailed information on T cell infiltration while maintaining high analysis efficiency through parallel computational processing rather than sequential simple readouts.
Data Source
AI summary
Embodiments of the present disclosure relate generally to methods used to perform diagnosis and drug screening because the method includes an algorithm that is able to obtain images of immune cells in 3D tissue cultures, analyze such images to create immune cell infiltration maps, automatically score and identify drugs or agents that can be potentially clinically beneficial.