Abstract：
OBJECTIVE: To evaluate the diagnostic performance and generalizability of the winning DL algorithm of the RSNA 2020 PE detection challenge to a local population using CTPA data from two hospitals.
METHODS: Consecutive CTPA images from patients referred for suspected PE were retrospectively analysed. The winning RSNA 2020 DL algorithm was retrained on the RSNA-STR Pulmonary Embolism CT (RSPECT) dataset. The algorithm was tested in hospital A on multidetector CT (MDCT) images of 238 patients and in hospital B on spectral detector CT (SDCT) and virtual monochromatic images (VMI) of 114 patients. The output of the DL algorithm was compared with a reference standard, which included a consensus reading by at least two experienced cardiothoracic radiologists for both hospitals. Areas under the receiver operating characteristic curve (AUCs) were calculated. Sensitivity and specificity were determined using the maximum Youden index.
RESULTS: According to the reference standard, PE was present in 73 patients (30.7%) in hospital A and 33 patients (29.0%) in hospital B. For the DL algorithm the AUC was 0.96 (95% CI 0.92-0.98) in hospital A, 0.89 (95% CI 0.81-0.94) for conventional reconstruction in hospital B and 0.87 (95% CI 0.80-0.93) for VMI.
CONCLUSIONS: The RSNA 2020 pulmonary embolism detection on CTPA challenge winning DL algorithm, retrained on the RSPECT dataset, showed high diagnostic accuracy on MDCT images. A somewhat lower performance was observed on SDCT images, which suggest additional training on novel CT technology may improve generalizability of this DL algorithm.
METHODS: Consecutive CTPA images from patients referred for suspected PE were retrospectively analysed. The winning RSNA 2020 DL algorithm was retrained on the RSNA-STR Pulmonary Embolism CT (RSPECT) dataset. The algorithm was tested in hospital A on multidetector CT (MDCT) images of 238 patients and in hospital B on spectral detector CT (SDCT) and virtual monochromatic images (VMI) of 114 patients. The output of the DL algorithm was compared with a reference standard, which included a consensus reading by at least two experienced cardiothoracic radiologists for both hospitals. Areas under the receiver operating characteristic curve (AUCs) were calculated. Sensitivity and specificity were determined using the maximum Youden index.
RESULTS: According to the reference standard, PE was present in 73 patients (30.7%) in hospital A and 33 patients (29.0%) in hospital B. For the DL algorithm the AUC was 0.96 (95% CI 0.92-0.98) in hospital A, 0.89 (95% CI 0.81-0.94) for conventional reconstruction in hospital B and 0.87 (95% CI 0.80-0.93) for VMI.
CONCLUSIONS: The RSNA 2020 pulmonary embolism detection on CTPA challenge winning DL algorithm, retrained on the RSPECT dataset, showed high diagnostic accuracy on MDCT images. A somewhat lower performance was observed on SDCT images, which suggest additional training on novel CT technology may improve generalizability of this DL algorithm.
摘要：
目的：使用来自两家医院的CTPA数据，评估RSNA2020PE检测挑战的成功DL算法对当地人群的诊断性能和通用性。
方法：回顾性分析疑似PE患者的连续CTPA图像。在RSNA-STR肺栓塞CT（RSPECT）数据集上重新训练了获胜的RSNA2020DL算法。该算法在医院A的238例患者的多探测器CT（MDCT）图像上进行了测试，在医院B的光谱探测器CT（SDCT）和114例患者的虚拟单色图像（VMI）上进行了测试。将DL算法的输出与参考标准进行比较，其中包括两家医院的至少两名经验丰富的心胸放射科医师的共识阅读。计算受试者工作特征曲线下面积(AUC)。使用最大Youden指数确定敏感性和特异性。
结果：根据参考标准，在医院A的73例患者（30.7％）和医院B的33例患者（29.0％）存在PE。对于DL算法，医院A的AUC为0.96（95％CI0.92-0.98），医院B的常规重建为0.89（95％CI0.81-0.94），VMI为0.87（95％CI0.80-0.93）。
结论：在CTPA挑战赢得DL算法的RSNA2020肺栓塞检测，在RSPECT数据集上重新训练，在MDCT图像上显示出较高的诊断准确性。在SDCT图像上观察到的性能略低，这表明对新型CT技术的额外训练可能会提高该DL算法的泛化性。
方法：回顾性分析疑似PE患者的连续CTPA图像。在RSNA-STR肺栓塞CT（RSPECT）数据集上重新训练了获胜的RSNA2020DL算法。该算法在医院A的238例患者的多探测器CT（MDCT）图像上进行了测试，在医院B的光谱探测器CT（SDCT）和114例患者的虚拟单色图像（VMI）上进行了测试。将DL算法的输出与参考标准进行比较，其中包括两家医院的至少两名经验丰富的心胸放射科医师的共识阅读。计算受试者工作特征曲线下面积(AUC)。使用最大Youden指数确定敏感性和特异性。
结果：根据参考标准，在医院A的73例患者（30.7％）和医院B的33例患者（29.0％）存在PE。对于DL算法，医院A的AUC为0.96（95％CI0.92-0.98），医院B的常规重建为0.89（95％CI0.81-0.94），VMI为0.87（95％CI0.80-0.93）。
结论：在CTPA挑战赢得DL算法的RSNA2020肺栓塞检测，在RSPECT数据集上重新训练，在MDCT图像上显示出较高的诊断准确性。在SDCT图像上观察到的性能略低，这表明对新型CT技术的额外训练可能会提高该DL算法的泛化性。
