Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers

• Main Authors: Jungheum Cho, Jihang Kim, Kyong Joon Lee, Chang Mo Nam, Sung Hyun Yoon, Hwayoung Song, Junghoon Kim, Ye Ra Choi, Kyung Hee Lee, Kyung Won Lee
• Format: Article
• Language: English
• Published: MDPI AG 2020-12-01
• Series: Journal of Clinical Medicine
• Subjects: lung neoplasms; deep learning; computer-aided diagnosis; multidetector computed tomography; early detection of cancer
• Online Access: https://www.mdpi.com/2077-0383/9/12/3908

We aimed to analyse the CT examinations of the previous screening round (CT_prev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CT_prev in participants with incidence lung cancer, and a DL-CAD analysed CT_prev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CT_prev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CT_prev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CT_prev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.