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

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...

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Autores principales:	Anum Masood, Po Yang, Bin Sheng, Huating Li, Ping Li, Jing Qin, Vitaveska Lanfranchi, Jinman Kim, David Dagan Feng
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Computer-aided diagnosis nodule detection cloud computing computed tomography lung cancer Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/fd08d16d02214fa28f91eb4d39f8fe1f
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spelling	oai:doaj.org-article:fd08d16d02214fa28f91eb4d39f8fe1f2021-11-19T00:00:25ZCloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT2168-237210.1109/JTEHM.2019.2955458https://doaj.org/article/fd08d16d02214fa28f91eb4d39f8fe1f2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8922769/https://doaj.org/toc/2168-2372Lung 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.Anum MasoodPo YangBin ShengHuating LiPing LiJing QinVitaveska LanfranchiJinman KimDavid Dagan FengIEEEarticleComputer-aided diagnosisnodule detectioncloud computingcomputed tomographylung cancerComputer applications to medicine. Medical informaticsR858-859.7Medical technologyR855-855.5ENIEEE Journal of Translational Engineering in Health and Medicine, Vol 8, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer-aided diagnosis nodule detection cloud computing computed tomography lung cancer Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
spellingShingle	Computer-aided diagnosis nodule detection cloud computing computed tomography lung cancer Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5 Anum Masood Po Yang Bin Sheng Huating Li Ping Li Jing Qin Vitaveska Lanfranchi Jinman Kim David Dagan Feng Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT
description	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.
format	article
author	Anum Masood Po Yang Bin Sheng Huating Li Ping Li Jing Qin Vitaveska Lanfranchi Jinman Kim David Dagan Feng
author_facet	Anum Masood Po Yang Bin Sheng Huating Li Ping Li Jing Qin Vitaveska Lanfranchi Jinman Kim David Dagan Feng
author_sort	Anum Masood
title	Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT
title_short	Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT
title_full	Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT
title_fullStr	Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT
title_full_unstemmed	Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT
title_sort	cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest ct
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/fd08d16d02214fa28f91eb4d39f8fe1f
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_version_	1718420705478967296

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

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