Kvak, D., Chromcová, A., Hrubý, R., Janů, E., Biroš, M., Pajdaković, M., ... & Strukov, S. (2023). Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images. Diagnostics, 13(6), 1043.
Abstract:
Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinomas and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors, and are prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD has achieved significantly higher sensitivity (0.910 (0.854-0.966)) than that of all assessed radiologists (RAD 1 0.290 (0.201-0.379), p<0.001, RAD 2 0.450 (0.352-0.548), p<0.001, RAD 3 0.670 (0.578-0.762), p<0.001, RAD 4 0.810 (0.733-0.887), p=0.025, RAD 5 0.700 (0.610-0.790), p<0.001). The DLAD specificity (0.775 (0.717-0.833)) was significantly lower than in all assessed radiologists (RAD 1 1.000 (0.984-1.000), p<0.001, RAD 2 0.970 (0.946-1.000), p<0.001, RAD 3 0.980 (0.961-1.000), p<0.001, RAD 4 0.975 (0.953-0.977), p<0.001, RAD 5 0.995 (0.985-1.000), p<0.001). The study results demonstrated that the proposed DLAD could be utilized as a decision-support system to reduce radiologists' false negative rate.
