Performance of Deep Learning Model in Detecting Operable Lung Cancer With Chest Radiographs
DC Field | Value | Language |
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dc.contributor.author | Cha, Min Jae | - |
dc.contributor.author | Chung, Myung Jin | - |
dc.contributor.author | Lee, Jeong Hyun | - |
dc.contributor.author | Lee, Kyung Soo | - |
dc.date.accessioned | 2021-06-18T07:40:51Z | - |
dc.date.available | 2021-06-18T07:40:51Z | - |
dc.date.issued | 2019-03 | - |
dc.identifier.issn | 0883-5993 | - |
dc.identifier.issn | 1536-0237 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/45127 | - |
dc.description.abstract | Purpose: The aim of this study was to evaluate the diagnostic performance of a trained deep convolutional neural network (DCNN) model for detecting operable lung cancer with chest radiographs (CXRs). Materials and Methods: The institutional review board approved this study. A deep learning model (DLM) based on DCNN was trained with 17,211 CXRs (5700 CT-confirmed lung nodules in 3500 CXRs and 13,711 normal CXRs), finally augmented to 600,000 images. For validation, a trained DLM was tested with 1483 CXRs with surgically resected lung cancer, marked and scored by 2 radiologists. Furthermore, diagnostic performances of DLM and 6 human observers were compared with 500 cases (200 visible T1 lung cancer on CXR and 300 normal CXRs) and analyzed using free-response receiver-operating characteristics curve (FROC) analysis. Results: The overall detection rate of DLM for resected lung cancers (27.2 +/- 14.6 mm) was a sensitivity of 76.8% (1139/1483) with a false positive per image (FPPI) of 0.3 and area under the FROC curve (AUC) of 0.732. In the comparison with human readers, DLM demonstrated a sensitivity of 86.5% at 0.1 FPPI and a sensitivity of 92% at 0.3 FPPI with AUC of 0.899 at an FPPI range of 0.03 to 0.44 for detecting visible T1 lung cancers, which were superior to the average of 6 human readers [mean sensitivity; 78% (range, 71.6% to 82.6%) at an FPPI of 0.1% and 85% (range, 80.2% to 89.2%) at an FPPI of 0.3, AUC of 0.819 (range, 0.754 to 0.862) at an FPPI of 0.03 to 0.44). Conclusions: A DLM has high diagnostic performance in detecting operable lung cancer with CXR, demonstrating a potential of playing a pivotal role for lung cancer screening. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | LIPPINCOTT WILLIAMS & WILKINS | - |
dc.title | Performance of Deep Learning Model in Detecting Operable Lung Cancer With Chest Radiographs | - |
dc.type | Article | - |
dc.identifier.doi | 10.1097/RTI.0000000000000388 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THORACIC IMAGING, v.34, no.2, pp 86 - 91 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000460363100003 | - |
dc.identifier.scopusid | 2-s2.0-85060391802 | - |
dc.citation.endPage | 91 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 86 | - |
dc.citation.title | JOURNAL OF THORACIC IMAGING | - |
dc.citation.volume | 34 | - |
dc.type.docType | Article | - |
dc.publisher.location | 파키스탄 | - |
dc.subject.keywordAuthor | deep learning model | - |
dc.subject.keywordAuthor | deep convolutional neural network | - |
dc.subject.keywordAuthor | chest radiograph | - |
dc.subject.keywordAuthor | pulmonary nodule | - |
dc.subject.keywordAuthor | lung cancer | - |
dc.subject.keywordPlus | COMPUTER-AIDED DETECTION | - |
dc.subject.keywordPlus | PULMONARY NODULES | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | SCHEME | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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