Artificial Intelligence-Based Model Exploiting Hematoxylin and Eosin Images to Predict Rare Gene Mutations in Patients With Lung Adenocarcinoma.

Abstract

Purpose: Accurately identifying gene mutations in lung cancer is crucial for treatment, while molecular diagnostic methods are time-consuming and complex. This study aims to develop an advanced deep learning model to address this issue.

Methods: In this study, the ResNeXt101 model framework was established to predict the gene mutation status in lung adenocarcinoma. The model was trained and validated using data from two cohorts: cohort 1, comprising 144 patients from the First Affiliated Hospital of China Medical University, and cohort 2, which includes 69 patients from the The Cancer Genome Atlas-Lung Adenocarcinoma public database. The model was trained and validated on the two data sets, respectively, and they served as external test sets for each other to further verify the performance of the model. Additionally, we tested the trained model on a metastatic cancer data set, which included metastases to organs outside the lungs. The performance of the model was evaluated using the AUC, accuracy, precision, recall, and F1 score.

Results: In cohort 1, the model achieved an AUC ranging from 0.93 to 1. In the external test on cohort 2, it performed well in predicting five of the six genes (AUC = 0.85-1). When tested on the metastatic cancer data set, it successfully predicted mutations of three of the six genes (AUC = 0.72-0.80).

Conclusion: The artificial intelligence model developed in this study has a high accuracy in predicting gene mutations in lung adenocarcinoma, which is conducive to improving the management of patients with lung adenocarcinoma and promoting precision medicine.