3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas.

Journal: Cancer research
Published Date: Oct 2, 2018

Abstract

: Identification of early-stage pulmonary adenocarcinomas before surgery, especially in cases of subcentimeter cancers, would be clinically important and could provide guidance to clinical decision making. In this study, we developed a deep learning system based on 3D convolutional neural networks and multitask learning, which automatically predicts tumor invasiveness, together with 3D nodule segmentation masks. The system processes a 3D nodule-centered patch of preprocessed CT and learns a deep representation of a given nodule without the need for any additional information. A dataset of 651 nodules with manually segmented voxel-wise masks and pathological labels of atypical adenomatous hyperplasia (AAH), adenocarcinomas (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA) was used in this study. We trained and validated our deep learning system on 523 nodules and tested its performance on 128 nodules. An observer study with 2 groups of radiologists, 2 senior and 2 junior, was also investigated. We merged AAH and AIS into one single category AAH-AIS, comprising a 3-category classification in our study. The proposed deep learning system achieved better classification performance than the radiologists; in terms of 3-class weighted average F1 score, the model achieved 63.3% while the radiologists achieved 55.6%, 56.6%, 54.3%, and 51.0%, respectively. These results suggest that deep learning methods improve the yield of discriminative results and hold promise in the CADx application domain, which could help doctors work efficiently and facilitate the application of precision medicine. SIGNIFICANCE: Machine learning tools are beginning to be implemented for clinical applications. This study represents an important milestone for this emerging technology, which could improve therapy selection for patients with lung cancer.

Authors

  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • Jiancheng Yang
    Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.
  • Yingli Sun
    Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
  • Cheng Li
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.
  • Weilan Wu
    Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, P.R. China.
  • Liang Jin
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Zhiming Yang
    Shenzhen Baoan Authentic TCM Therapy Hospital, Shenzhen, 518101, China.
  • Bingbing Ni
    Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.
  • Pan Gao
    College of Information Science and Technology, Shihezi University, Shihezi 832003, China.
  • Peijun Wang
    Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, P.R. China.
  • Yanqing Hua
    Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, P.R. China. minli77@163.com huayq007@163.com.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.

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