Cited 2 times in
Automated Computer-Aided Detection of Lung Nodules in Metastatic Colorectal Cancer Patients for the Identification of Pulmonary Oligometastatic Disease
DC Field | Value | Language |
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dc.contributor.author | 김진성 | - |
dc.contributor.author | 박성용 | - |
dc.contributor.author | 서영주 | - |
dc.contributor.author | 이병민 | - |
dc.contributor.author | 장지석 | - |
dc.contributor.author | 이준복 | - |
dc.date.accessioned | 2023-03-03T02:16:06Z | - |
dc.date.available | 2023-03-03T02:16:06Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 0360-3016 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/192789 | - |
dc.description.abstract | Purpose: This study aimed to explore the possibility and clinical utility of existing artificial intelligence (AI)-based computer-aided detection (CAD) of lung nodules to identify pulmonary oligometastases. Patients and methods: The chest computed tomography (CT) scans of patients with lung metastasis from colorectal cancer between March 2006 and November 2018 were analyzed. The patients were selected from a database of 1395 patients and studied in 2 cohorts. The first cohort included 50 patients, and the CT scans of these patients were independently evaluated for lung-nodule (≥3 mm) detection by a CAD-assisted radiation oncologist (CAD-RO) as well as by an expert radiologist. Interobserver variability by 2 additional radiation oncologists and 2 thoracic surgeons were also measured. In the second cohort of 305 patients, survival outcomes were evaluated based on the number of CAD-RO-detected nodules. Results: In the first cohort, the sensitivity and specificity of the CAD-RO for identifying oligometastatic disease (OMD) from varying criteria by ≤2 nodules, ≤3 nodules, ≤4 nodules, and ≤5 nodules were 71.9% and 88.9%, 82.9% and 93.3%, 97.1% and 73.3%, and 97.5% and 90.0%, respectively. The sensitivity of the CAD-RO in the nodule detection compared with the radiologist was 81.6%. The average (standard deviation) sensitivity in interobserver variability analysis was 80.0% (3.7%). In the second cohort, the 5-year survival rates of patients with 1, 2, 3, 4, or ≥5 metastatic nodules were 75.2%, 52.9%, 45.7%, 29.1%, and 22.7%, respectively. Conclusions: Proper identification of the pulmonary OMD and the correlation between the number of CAD-RO-detected nodules and survival suggest the potential practicality of AI in OMD recognition. Developing a deep learning-based model specific to the metastatic setting, which enables a quick estimation of disease burden and identification of OMD, is underway. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Elsevier Science Inc. | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Artificial Intelligence | - |
dc.subject.MESH | Colorectal Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Computers | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Lung | - |
dc.subject.MESH | Lung Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Radiographic Image Interpretation, Computer-Assisted / methods | - |
dc.subject.MESH | Sensitivity and Specificity | - |
dc.subject.MESH | Solitary Pulmonary Nodule* / diagnostic imaging | - |
dc.subject.MESH | Tomography, X-Ray Computed / methods | - |
dc.title | Automated Computer-Aided Detection of Lung Nodules in Metastatic Colorectal Cancer Patients for the Identification of Pulmonary Oligometastatic Disease | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiation Oncology (방사선종양학교실) | - |
dc.contributor.googleauthor | Jason Joon Bock Lee | - |
dc.contributor.googleauthor | Young Joo Suh | - |
dc.contributor.googleauthor | Caleb Oh | - |
dc.contributor.googleauthor | Byung Min Lee | - |
dc.contributor.googleauthor | Jin Sung Kim | - |
dc.contributor.googleauthor | Yongjin Chang | - |
dc.contributor.googleauthor | Yeong Jeong Jeon | - |
dc.contributor.googleauthor | Ji Young Kim | - |
dc.contributor.googleauthor | Seong Yong Park | - |
dc.contributor.googleauthor | Jee Suk Chang | - |
dc.identifier.doi | 10.1016/j.ijrobp.2022.08.042 | - |
dc.contributor.localId | A04548 | - |
dc.contributor.localId | A01508 | - |
dc.contributor.localId | A01892 | - |
dc.contributor.localId | A05931 | - |
dc.contributor.localId | A04658 | - |
dc.relation.journalcode | J01157 | - |
dc.identifier.eissn | 1879-355X | - |
dc.identifier.pmid | 36028066 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0360301622031613 | - |
dc.contributor.alternativeName | Kim, Jinsung | - |
dc.contributor.affiliatedAuthor | 김진성 | - |
dc.contributor.affiliatedAuthor | 박성용 | - |
dc.contributor.affiliatedAuthor | 서영주 | - |
dc.contributor.affiliatedAuthor | 이병민 | - |
dc.contributor.affiliatedAuthor | 장지석 | - |
dc.citation.volume | 114 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1045 | - |
dc.citation.endPage | 1052 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, Vol.114(5) : 1045-1052, 2022-12 | - |
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