Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population

• Published in: PloS one Vol. 17; no. 5; p. e0266799
• Main Authors: Murchison, John T., Ritchie, Gillian, Senyszak, David, Nijwening, Jeroen H., van Veenendaal, Gerben, Wakkie, Joris, van Beek, Edwin J. R.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.05.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; CAI; Calcification; Cancer screening; Chest; Classification; Clinical medicine; Computed tomography; Computer and Information Sciences; Computer assisted instruction; Computers; Deep Learning; Early Detection of Cancer; Engineering and Technology; Evaluation; Growth rate; Humans; Lung; Lung cancer; Lung diseases; Lung Neoplasms - diagnostic imaging; Lung nodules; Machine learning; Mathematical analysis; Medical diagnosis; Medical imaging; Medical screening; Medicine and Health Sciences; Multiple Pulmonary Nodules - diagnostic imaging; Nodules; People and Places; Population studies; Radiographic Image Interpretation, Computer-Assisted; Readers; Research and Analysis Methods; Retrospective Studies; Segmentation; Sensitivity and Specificity; Solitary Pulmonary Nodule - diagnostic imaging; Thorax; Tomography, X-Ray Computed - methods; Scotland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0266799

In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. Here, we evaluated the CAD in a retrospective cohort of a routine clinical population. In total, a number of 337 scans of 314 different subjects with reported nodules of 3-30 mm in size were included into the evaluation. Two independent thoracic radiologists alternately reviewed scans with or without CAD assistance to detect, classify, segment, and register pulmonary nodules. A third, more experienced, radiologist served as an adjudicator. In addition, the cohort was analyzed by the CAD alone. The study cohort was divided into five different groups: 1) 178 CT studies without reported pulmonary nodules, 2) 95 studies with 1-10 pulmonary nodules, 23 studies from the same patients with 3) baseline and 4) follow-up studies, and 5) 18 CT studies with subsolid nodules. A reference standard for nodules was based on majority consensus with the third thoracic radiologist as required. Sensitivity, false positive (FP) rate and Dice inter-reader coefficient were calculated. After analysis of 470 pulmonary nodules, the sensitivity readings for radiologists without CAD and radiologist with CAD, were 71.9% (95% CI: 66.0%, 77.0%) and 80.3% (95% CI: 75.2%, 85.0%) (p < 0.01), with average FP rate of 0.11 and 0.16 per CT scan, respectively. Accuracy and kappa of CAD for classifying solid vs sub-solid nodules was 94.2% and 0.77, respectively. Average inter-reader Dice coefficient for nodule segmentation was 0.83 (95% CI: 0.39, 0.96) and 0.86 (95% CI: 0.51, 0.95) for CAD versus readers. Mean growth percentage discrepancy of readers and CAD alone was 1.30 (95% CI: 1.02, 2.21) and 1.35 (95% CI: 1.01, 4.99), respectively. The applied CAD significantly increased radiologist's detection of actionable nodules yet also minimally increasing the false positive rate. The CAD can automatically classify and quantify nodules and calculate nodule growth rate in a cohort of a routine clinical population. Results suggest this Deep Learning software has the potential to assist chest radiologists in the tasks of pulmonary nodule detection and management within their routine clinical practice.