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Automated Computer-Aided Detection of Lung Nodules in Metastatic Colorectal Cancer Patients for the Identification of Pulmonary Oligometastatic Disease

Authors
 Jason Joon Bock Lee  ;  Young Joo Suh  ;  Caleb Oh  ;  Byung Min Lee  ;  Jin Sung Kim  ;  Yongjin Chang  ;  Yeong Jeong Jeon  ;  Ji Young Kim  ;  Seong Yong Park  ;  Jee Suk Chang 
Citation
 INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, Vol.114(5) : 1045-1052, 2022-12 
Journal Title
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
ISSN
 0360-3016 
Issue Date
2022-12
MeSH
Artificial Intelligence ; Colorectal Neoplasms* / diagnostic imaging ; Computers ; Humans ; Lung ; Lung Neoplasms* / diagnostic imaging ; Radiographic Image Interpretation, Computer-Assisted / methods ; Sensitivity and Specificity ; Solitary Pulmonary Nodule* / diagnostic imaging ; Tomography, X-Ray Computed / methods
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.
Full Text
https://www.sciencedirect.com/science/article/pii/S0360301622031613
DOI
10.1016/j.ijrobp.2022.08.042
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Thoracic and Cardiovascular Surgery (흉부외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jinsung(김진성) ORCID logo https://orcid.org/0000-0003-1415-6471
Park, Seong Yong(박성용) ORCID logo https://orcid.org/0000-0002-5180-3853
Suh, Young Joo(서영주) ORCID logo https://orcid.org/0000-0002-2078-5832
Lee, Byung Min(이병민)
Lee, Jason Joon Bock(이준복)
Chang, Jee Suk(장지석) ORCID logo https://orcid.org/0000-0001-7685-3382
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/192789
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