Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

Cancer Medicine

Jiancheng Yang¹  Bingbing Ni¹  Dexi Bi²  Pan Gao³  Yanqing Hua³  Ming Li³  Yingli Sun³  Cheng Li³  Liang Jin³  Wei Zhao³  Xiaoxia Zhu³  Peijun Wang⁴  Mengdi Xu⁵ 

[1] Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China;Department of Pathology Shanghai Tenth People's Hospital, Tongji University School of Medicine Shanghai China;Department of Radiology Huadong Hospital Affiliated to Fudan University Shanghai China;Department of Radiology, School of Medicine Tongji Hospital, Tongji University Shanghai China;Diannei Technology Shanghai China;

关键词: convolutional neural networks;    deep learning;    EGFR;    mixup training technique;    radiomics;   

DOI  :  10.1002/cam4.2233

来源: DOAJ

【 摘 要 】

Abstract To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR‐mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild‐type (WT) was retrospectively analyzed. A deep learning system, namely 3D DenseNets, was developed to process 3D patches of nodules from CT data, and learn strong representations with supervised end‐to‐end training. The 3D DenseNets were trained with a training subset of 348 nodules and tuned with a development subset of 116 nodules. A strong data augmentation technique, mixup, was used for better generalization. We evaluated our model on a holdout subset of 115 nodules. An independent public dataset of 37 nodules from the cancer imaging archive (TCIA) was also used to test the generalization of our method. Conventional radiomics analysis was also performed for comparison. Our method achieved promising performance on predicting EGFR mutation status, with AUCs of 75.8% and 75.0% for our holdout test set and public test set, respectively. Moreover, strong relations were found between deep learning feature and conventional radiomics, while deep learning worked through an enhanced radiomics manner, that is, deep learned radiomics (DLR), in terms of robustness, compactness and expressiveness. The proposed deep learning system predicts EGFR‐mutant of lung adenocarcinomas in CT images noninvasively and automatically, indicating its potential to help clinical decision‐making by identifying eligible patients of pulmonary adenocarcinoma for EGFR‐targeted therapy.

【 授权许可】

Unknown