PDF(Pubmed)
Abstract：
Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs.
This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively.
For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.
This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively.
For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.
摘要：
目的：计算机断层扫描（CT）是评估孤立性肺结节（SPN）的首选方法，但可能缺乏访问或可用性，此外,重叠的解剖结构会阻碍在胸片上检测SPN。我们开发并评估了一种深度学习算法的临床可行性，该算法可从数字重建的正面和侧面射线照片（DRR）生成胸部的数字重建断层扫描（DRT）图像，并使用它们来检测SPN。
方法：这项单机构回顾性研究包括637例胸部非对比螺旋CT患者（平均年龄68岁，中位年龄69岁，标准偏差11.7年；355名女性）在2012年11月至2020年12月之间，SPNs测量为10-30mm。对562名患者进行了深度学习模型的训练，对60名患者进行了验证，并对其余15名患者进行了测试。平面射线照相（DRR和CT扫描图，PR）单独或与DRT一起由两名放射科医生以独立的盲法进行评估。DRTSPN图像在结节大小和位置方面的质量，形态学,并评估了不透明度，结果：DRT加PR的诊断性能高于单独的PR（受试者工作特征曲线下面积0.95-0.98vs.0.80-0.85；p<0.05）。DRT加PR使SPN的诊断比单独的PR多11例。DRT加PR的观察员间协议为0.82，仅PR的观察员间协议为0.89；以及观察员间的大小和位置协议，形态学,DRTSPN的不透明度分别为0.94、0.68和0.38。
结论：对于SPN检测，DRT加PR显示出比单独PR更好的诊断性能。深度学习可用于生成DRT图像并改善SPN的检测。
方法：这项单机构回顾性研究包括637例胸部非对比螺旋CT患者（平均年龄68岁，中位年龄69岁，标准偏差11.7年；355名女性）在2012年11月至2020年12月之间，SPNs测量为10-30mm。对562名患者进行了深度学习模型的训练，对60名患者进行了验证，并对其余15名患者进行了测试。平面射线照相（DRR和CT扫描图，PR）单独或与DRT一起由两名放射科医生以独立的盲法进行评估。DRTSPN图像在结节大小和位置方面的质量，形态学,并评估了不透明度，结果：DRT加PR的诊断性能高于单独的PR（受试者工作特征曲线下面积0.95-0.98vs.0.80-0.85；p<0.05）。DRT加PR使SPN的诊断比单独的PR多11例。DRT加PR的观察员间协议为0.82，仅PR的观察员间协议为0.89；以及观察员间的大小和位置协议，形态学,DRTSPN的不透明度分别为0.94、0.68和0.38。
结论：对于SPN检测，DRT加PR显示出比单独PR更好的诊断性能。深度学习可用于生成DRT图像并改善SPN的检测。
