Deep Learning Lung Cancer Subtype Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The distinction between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) in lung cancer diagnosis is challenging due to the lack of definitive histologic features, requiring time-consuming visual inspection and confirmatory immunohistochemical stains, and existing automatic analysis methods have limited accuracy.
Innovation Solution
A deep convolutional neural network (Inception V3) is trained on whole-slide images to classify lung cancer into LUAD, LUSC, or normal tissue with high accuracy, predicting mutations in commonly mutated genes, and generating heatmaps for spatial heterogeneity, assisting pathologists in subtype classification and treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection by experienced pathologists is used to distinguish LUAD and LUSC, then diagnostic accuracy can be achieved, but the process is time-consuming and requires confirmatory immunohistochemical stains
Solution Approach 1:
The patent replaces the mechanical visual inspection process with an automated deep learning system. The Inception V3 convolutional neural network processes histopathology images automatically, eliminating the need for time-consuming manual examination by pathologists while maintaining high classification accuracy between LUAD and LUSC subtypes
Solution Approach 2:
The deep learning model performs preliminary classification of lung cancer subtypes before confirmatory testing is needed. By pre-analyzing histopathology images and predicting cancer subtype with high accuracy, the system identifies cases that likely don't require additional immunohistochemical stains, thereby reducing overall diagnosis time
2Reliability
If confirmatory immunohistochemical stains are performed to achieve accurate classification, then diagnostic reliability improves, but the complexity and cost of the diagnostic process increases
Solution Approach 1:
The patent substitutes complex immunohistochemical staining procedures with a computational deep learning system. The Inception V3 model analyzes histopathology images and provides reliable cancer subtype classification without requiring additional laboratory reagents, equipment, or complex staining protocols, thereby simplifying the diagnostic workflow while maintaining high reliability
3Measurement precision
If deep learning models are trained on large datasets to improve classification accuracy, then measurement precision improves, but the computational resources and training time required increase
Solution Approach 1:
The patent employs a pre-trained Inception V3 model that was originally developed for general image recognition tasks. This universal model has already learned robust feature extraction from large-scale image datasets (ImageNet), and can be transferred to the specialized task of lung cancer classification. This transfer learning approach allows the system to achieve high accuracy without requiring extensive retraining on large medical datasets, thereby reducing computational energy consumption
Data Source
AI summary
Systems and methods for classification and mutation prediction from histopathology images using deep learning. In particular, the examples described herein utilize lung cancer as an example, in particular adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) as two subtypes of lung cancer to identify and distinguish between.