Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, Turin, Italy.
Department of Surgical Sciences, University of Turin, A.O.U. Città della Salute e della Scienza, Via Genova 3, 10126, Turin, Italy.
Eur Radiol. 2019 Jan;29(1):144-152. doi: 10.1007/s00330-018-5528-6. Epub 2018 Jun 15.
To compare unassisted and CAD-assisted detection and time efficiency of radiologists in reporting lung nodules on CT scans taken from patients with extra-thoracic malignancies using a Cloud-based system.
Three radiologists searched for pulmonary nodules in patients with extra-thoracic malignancy who underwent CT (slice thickness/spacing 2 mm/1.7 mm) between September 2015 and March 2016. All nodules detected by unassisted reading were measured and coordinates were uploaded on a cloud-based system. CAD marks were then reviewed by the same readers using the cloud-based interface. To establish the reference standard all nodules ≥ 3 mm detected by at least one radiologist were validated by two additional experienced radiologists in consensus. Reader detection rate and reporting time with and without CAD were compared. The study was approved by the local ethics committee. All patients signed written informed consent.
The series included 225 patients (age range 21-90 years, mean 62 years), including 75 patients having at least one nodule, for a total of 215 nodules. Stand-alone CAD sensitivity for lesions ≥ 3 mm was 85% (183/215, 95% CI: 82-91); mean false-positive rate per scan was 3.8. Sensitivity across readers in detecting lesions ≥ 3 mm was statistically higher using CAD: 65% (95% CI: 61-69) versus 88% (95% CI: 86-91, p<0.01). Reading time increased by 11% using CAD (296 s vs. 329 s; p<0.05).
In patients with extra-thoracic malignancies, CAD-assisted reading improves detection of ≥ 3-mm lung nodules on CT, slightly increasing reading time.
• CAD-assisted reading improves the detection of lung nodules compared with unassisted reading on CT scans of patients with primary extra-thoracic tumour, slightly increasing reading time. • Cloud-based CAD systems may represent a cost-effective solution since CAD results can be reviewed while a separated cloud back-end is taking care of computations. • Early identification of lung nodules by CAD-assisted interpretation of CT scans in patients with extra-thoracic primary tumours is of paramount importance as it could anticipate surgery and extend patient life expectancy.
比较基于云的系统辅助检测和无辅助检测以及放射科医师报告胸部外恶性肿瘤 CT 扫描中肺结节的时间效率。
2015 年 9 月至 2016 年 3 月，3 名放射科医师对接受胸部外恶性肿瘤 CT 检查（层厚/层间距 2mm/1.7mm）的患者进行肺结节搜索。通过独立阅读检测到的所有结节均进行测量，并将坐标上传到基于云的系统。然后，同一读者使用基于云的界面查看 CAD 标记。为了建立参考标准，所有至少有 1 名放射科医师检测到的≥3mm 的结节由另外 2 名有经验的放射科医师进行共识验证。比较有和没有 CAD 时的读者检出率和报告时间。该研究获得了当地伦理委员会的批准。所有患者均签署了书面知情同意书。
该系列包括 225 名患者（年龄范围 21-90 岁，平均 62 岁），其中 75 名患者至少有一个结节，共 215 个结节。单独使用 CAD 检测≥3mm 病变的敏感性为 85%（183/215，95%CI：82-91）；平均每扫描的假阳性率为 3.8。使用 CAD 检测≥3mm 病变的读者检测敏感性具有统计学意义：65%（95%CI：61-69）与 88%（95%CI：86-91，p<0.01）。使用 CAD 时阅读时间增加 11%（296s 与 329s，p<0.05）。
在胸部外恶性肿瘤患者中，CAD 辅助阅读可提高 CT 扫描中≥3mm 肺结节的检出率，略微增加阅读时间。
在原发性胸部外肿瘤患者的 CT 扫描中，CAD 辅助阅读可提高与独立阅读相比，肺结节的检出率，略微增加阅读时间。
基于云的 CAD 系统可能是一种具有成本效益的解决方案，因为在单独的云后端处理计算的同时，可以审查 CAD 结果。
胸部外原发性肿瘤患者 CT 扫描中 CAD 辅助解读早期识别肺结节至关重要，因为它可以提前手术并延长患者预期寿命。
