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

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
International Journal of Radiation Oncology, Biology, Physic
Related Product


Date Published

This study aimed to investigate the potential and clinical utility of artificial intelligence (AI)–based computer-aided detection (CAD) of lung nodules in identifying pulmonary oligometastases. The chest CT scans of patients with lung metastasis from colorectal cancer were analyzed in two cohorts. In the first cohort, the CAD-assisted radiation oncologist (CAD-RO) demonstrated sensitivity and specificity for identifying oligometastatic disease (OMD) compared to an expert radiologist, with the sensitivity of the CAD-RO in nodule detection at 81.6%. The interobserver variability analysis showed an average sensitivity of 80.0%. In the second cohort, the 5-year survival rates were evaluated based on the number of CAD-RO–detected nodules, revealing a correlation between the number of nodules and survival. The study concluded that the use of AI, particularly employing the AVIEW LCS nodule detection AI, in OMD recognition demonstrates promising practicality, and the development of a deep learning–based model specific to the metastatic setting is underway.


제품, 인재 채용, 투자 관련 또는 기타 문의사항이 있으신 경우 편하신 방법으로 연락주시기 바랍니다