{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T01:46:45Z","timestamp":1768528005174,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004332","name":"Seoul National University Hospital","doi-asserted-by":"publisher","award":["06-2016-3000"],"award-info":[{"award-number":["06-2016-3000"]}],"id":[{"id":"10.13039\/501100004332","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Pulm Med"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Diagnosis of pneumonia is critical in managing patients with febrile neutropenia (FN), however, chest X-ray (CXR) has limited performance in the detection of pneumonia. We aimed to evaluate the performance of a deep learning-based computer-aided detection (CAD) system in pneumonia detection in the CXRs of consecutive FN patients and investigated whether CAD could improve radiologists\u2019 diagnostic performance when used as a second reader.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>CXRs of patients with FN (a body temperature\u2009\u2265\u200938.3\u00a0\u00b0C, or a sustained body temperature\u2009\u2265\u200938.0\u00a0\u00b0C for an hour; absolute neutrophil count\u2009&lt;\u2009500\/mm<jats:sup>3<\/jats:sup>) obtained between January and December 2017 were consecutively included, from a single tertiary referral hospital. Reference standards for the diagnosis of pneumonia were defined by consensus of two thoracic radiologists after reviewing medical records and CXRs. A commercialized, deep learning-based CAD system was retrospectively applied to detect pulmonary infiltrates on CXRs. For comparing performance, five radiologists independently interpreted CXRs initially without the CAD results (radiologist-alone interpretation), followed by the interpretation with CAD. The sensitivities and specificities for detection of pneumonia were compared between radiologist-alone interpretation and interpretation with CAD. The standalone performance of the CAD was also evaluated, using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Moreover, sensitivity and specificity of standalone CAD were compared with those of radiologist-alone interpretation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Among 525 CXRs from 413 patients (52.3% men; median age 59\u00a0years), pneumonia was diagnosed in 128 (24.4%) CXRs. In the interpretation with CAD, average sensitivity of radiologists was significantly improved (75.4% to 79.4%, <jats:italic>P<\/jats:italic>\u2009=\u20090.003) while their specificity remained similar (75.4% to 76.8%, <jats:italic>P<\/jats:italic>\u2009=\u20090.101), compared to radiologist-alone interpretation. The CAD exhibited AUC, sensitivity, and specificity of 0.895, 88.3%, and 68.3%, respectively. The standalone CAD exhibited higher sensitivity (86.6% vs. 75.2%, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) and lower specificity (64.8% vs. 75.4%, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) compared to radiologist-alone interpretation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>In patients with FN, the deep learning-based CAD system exhibited radiologist-level performance in detecting pneumonia on CXRs and enhanced radiologists\u2019 performance.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12890-021-01768-0","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T16:04:47Z","timestamp":1638893087000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study"],"prefix":"10.1186","volume":"21","author":[{"given":"Eui Jin","family":"Hwang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong Hyuk","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae Hyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Woo Hyeon","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Mo","family":"Goo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang Min","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"issue":"suppl 5","key":"1768_CR1","doi-asserted-by":"publisher","first-page":"v111","DOI":"10.1093\/annonc\/mdw325","volume":"27","author":"J Klastersky","year":"2016","unstructured":"Klastersky J, de Naurois J, Rolston K, Rapoport B, Maschmeyer G, Aapro M, Herrstedt J, Committee EG. 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The requirement for patients\u2019 informed consents was waived by the institutional review board of Seoul National University Hospital. The present study was carried out in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"EJH reports research grant from Lunit Inc., outside the present study. JHL reports no competing interest. JHK reports no competing interest. WHL reports no competing interest. JMG reports no competing interest. CMP reports research grant from Lunit Inc., outside the present study, and holds stock options of Lunit Inc. and Coreline Soft.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"406"}}