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
Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
Qure.ai, Level 7, Oberoi Commerz II, Goregaon East, Mumbai, 400063, India.
Eur Radiol. 2025 Feb;35(2):1076-1088. doi: 10.1007/s00330-024-10969-0. Epub 2024 Jul 23.
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.
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.
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.
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.
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.
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.
本研究旨在对一种商用计算机辅助检测（CAD）系统进行外部验证，该系统用于在CT扫描中自动检测和鉴别实性、部分实性和磨玻璃肺结节（LN）。
这项回顾性研究涵盖了2020年1月至2021年12月在一家荷兰大学医院进行的263例胸部CT扫描。所有扫描均由一名放射科医生（R1）阅片，并与初始放射学报告进行比较。存在争议的扫描由一名裁决放射科医生（R2）评估。所有扫描也由CAD进行处理。计算CAD在检测方面的灵敏度和假阳性（FP）率的独立性能，以及鉴别方面的灵敏度，包括纹理、钙化、毛刺和位置。同时评估R1的检测灵敏度。
在121例含有结节的扫描（142例不含结节的扫描）中总共识别出183个真结节，其中R1识别出165/183（90.2%）。CAD检测出149个结节，其中12个未被R1识别，灵敏度达到149/183（81.4%），FP率为49/121（0.405）。CAD对实性、部分实性和磨玻璃LN的检测灵敏度分别为82/94（87.2%）、42/47（89.4%）和25/42（59.5%）。实性、部分实性和磨玻璃LN的分类准确率分别为81/82（98.8%）、16/42（38.1%）和18/25（72.0%）。此外，CAD在钙化、毛刺和位置方面的总体分类准确率分别为137/149（91.9%）、123/149（82.6%）和141/149（94.6%）。
尽管该系统的总体检测率略落后于放射科医生，但CAD能够检测不同的LN，因此有潜力提高阅片者的检测率。虽然获得了有前景的鉴别性能，但该工具在纹理分类方面的性能仍是一个值得关注的问题。
有许多商用肺结节计算机辅助检测系统，其中一些仅基于其对实性结节的检测性能进行了外部验证。我们鼓励研究人员通过纳入所有相关特征，包括部分实性和磨玻璃结节，来评估性能。
很少有计算机辅助检测（CAD）系统针对肺结节的自动检测和鉴别进行外部验证。使用CAD测量的检测灵敏度为81.4%，总体纹理分类灵敏度为77.2%。CAD有潜力提高单阅片者的检测率，然而，纹理分类方面需要改进。
