TY - JOUR
T1 - Automated Computer-Aided Detection of Lung Nodules in Metastatic Colorectal Cancer Patients for the Identification of Pulmonary Oligometastatic Disease
AU - Lee, Jason Joon Bock
AU - Suh, Young Joo
AU - Oh, Caleb
AU - Lee, Byung Min
AU - Kim, Jin Sung
AU - Chang, Yongjin
AU - Jeon, Yeong Jeong
AU - Kim, Ji Young
AU - Park, Seong Yong
AU - Chang, Jee Suk
N1 - Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85139013585
U2 - 10.1016/j.ijrobp.2022.08.042
DO - 10.1016/j.ijrobp.2022.08.042
M3 - Article
C2 - 36028066
AN - SCOPUS:85139013585
SN - 0360-3016
VL - 114
SP - 1045
EP - 1052
JO - International Journal of Radiation Oncology Biology Physics
JF - International Journal of Radiation Oncology Biology Physics
IS - 5
ER -
