Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning Techniques - Huang, Kaimei; Mo, Zhiyi; Zhu, Wen; Liao, Bo; Yang, Yachao; Wu, Fang-Xiang (Frontiers in Oncology, 2021)
Lung cancer is a kind of cancer with high morbidity and mortality which is associated with various gene mutations. Individualized targeted-drug therapy has become the optimized treatment of lung cancer, especially benefit for patients who are not qualified for lung lobectomy. Targeted-drug therapy is relatively optimal treatment option because of high efficiency and specificity, and the toxicity is relatively lower compared to empirical chemotherapy. It is crucial to accurately identify mutant genes within tumor region from stained pathological slice. Therefore, we mainly focus on identifying mutant gene of lung cancer by analyzing the pathological images. In this study, 180 hematoxylin-eosin staining (H&E) images of lung cancer were downloaded from the Cancer Gene Atlas (TCGA). The recently published novel DeepLRHE framework was the study to predict the recurrence from H&E stained imaging and achieved good accuracy. It demonstrated that our method can be used to choose targeted-drug therapy which might be applied to clinical practice.