Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT

Anum Masood; Po Yang; Bin Sheng; Huating Li; Ping Li; Jing Qin; Vitaveska Lanfranchi; Jinman Kim; David Dagan Feng

doi:10.1109/JTEHM.2019.2955458

Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT

IEEE J Transl Eng Health Med. 2019 Dec 4:8:4300113. doi: 10.1109/JTEHM.2019.2955458. eCollection 2020.

Authors

Anum Masood¹, Po Yang², Bin Sheng¹, Huating Li³, Ping Li⁴, Jing Qin⁵, Vitaveska Lanfranchi², Jinman Kim⁶, David Dagan Feng⁶

Affiliations

¹ 1Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China.
² 2Department of Computer ScienceUniversity of SheffieldSheffieldS1 4DPU.K.
³ 3Shanghai Jiao Tong University Affiliated Sixth People's HospitalShanghai200233China.
⁴ 4Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong.
⁵ 5Centre for Smart Health, School of NursingThe Hong Kong Polytechnic UniversityHong Kong.
⁶ 6Biomedical and Multimedia Information Technology Research Group, School of Information TechnologiesThe University of SydneySydneyNSW2006Australia.

Abstract

Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People's Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients.

Keywords: Computer-aided diagnosis; cloud computing; computed tomography; lung cancer; nodule detection.

Grants and funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61872241, Grant 61572316, and Grant 81870598, the National Key Research and Development Program of China under Grant 2017YFE0104000 and Grant 2016YFC1300302, The Hong Kong Polytechnic University under Grant P0030419 and Grant P0030929, and the Science and Technology Commission of Shanghai Municipality under Grant 18410750700, Grant 17411952600, and Grant 16DZ0501100.