Assessment of artificial intelligence-aided computed tomography in lung cancer screening

Abstract

Background: Lung cancer is one of the most common causes of cancer-related deaths in developed and developing countries. Therefore, early detection of lung cancer has a significant impact on lung cancer surveillance. Interpretation of lung CT scans for cancer screening is considered an intensive task for most radiologists, and long experience is required for accurate diagnosis through visual processing. This cross-sectional study introduces automated CAD software (Careline Soft’s AVIEW Metric software). This software can detect and classify lung nodules in CT scans. The performance of a deep learning (DL) model embedded in that software will be compared with that of the radiologists. Also, the feasibility of lung cancer screening protocol is evaluated in Suez Canal University Hospital, Ismailia, Egypt, by implementing Lung Imaging Reporting and Data System (Lung-RADS). Results: As for the detection of the pulmonary nodules, the initial review by the CAD system (without validation by the researcher radiologist) has high sensitivity (93.0%) and specificity (95.5%) with overall accuracy of 93.6%. After review of the automatically detected nodules by the researcher radiologist was done, the final CAD has higher sensitivity (98.2%) and comparable specificity (95.5%) for the detection of pulmonary nodules with overall accuracy of 97.4%. As for lung cancer screening (categorization of Lung-RADS 3 and 4 nodules), unrevised initial computer-aided detection has 97.9% specificity and 96.9% for lung cancer screening with overall accuracy of 97.4%. After second look and review of the CAD result by the researcher radiologist, there is total agreement in total number of nodules and categorization of Lung-RADS 3 and 4. This gives an excellent agreement of 88.6% (κ = 0.951) between the CAD system and reference radiologist in the overall categorization of all lung nodules according to Lung-RADS classification. Conclusions: The application of CAD system demonstrated increased sensitivity and specificity for the detection of lung nodules and total agreement in the detection of suspicious and probably benign nodules (lung cancer screening) and excellent level of agreement in the overall lung nodule categorization (Lung-RADS).