Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans

• Published in: European radiology Vol. 35; no. 2; pp. 1076 - 1088
• Main Authors: Paramasamy, Jasika, Mandal, Souvik, Blomjous, Maurits, Mulders, Ties, Bos, Daniel, Aerts, Joachim G. J. V., Vanapalli, Prakash, Challa, Vikash, Sathyamurthy, Saigopal, Devi, Ranjana, Jain, Ritvik, Visser, Jacob J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025; Springer Nature B.V
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Artificial intelligence; CAD; Calcification; Classification; Computed tomography; Computer aided design; Diagnostic Radiology; Female; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung nodules; Lungs; Male; Medical imaging; Medical prognosis; Medicine; Medicine & Public Health; Middle Aged; Multiple Pulmonary Nodules - diagnostic imaging; Neuroradiology; Nodules; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Reproducibility of Results; Retrospective Studies; Sensitivity analysis; Sensitivity and Specificity; Software; Solitary Pulmonary Nodule - diagnostic imaging; Statistical analysis; Texture; Tomography, X-Ray Computed - methods; Ultrasound; Early detection of cancer; Lung; Lung cancer; Computer-assisted detection; Validation study
• DOI: 10.1007/s00330-024-10969-0
• ISSN: 1432-1084; 0938-7994

Objectives This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans. Methods This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1’s detection sensitivity was also assessed. Results A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD’s detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively. Conclusions Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader’s detection rate. While promising characterization performances are obtained, the tool’s performance in terms of texture classification remains a subject of concern. Clinical relevance statement Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules. Key Points Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required. Graphical Abstract