Abstract：
OBJECTIVE: To propose a convolutional neural network (EmbNet) for automatic pulmonary embolism detection on computed tomography pulmonary angiogram (CTPA) scans and to assess its diagnostic performance.
METHODS: 305 consecutive CTPA scans between January 2019 and December 2021 were enrolled in this study (142 for training, 163 for internal validation), and 250 CTPA scans from a public dataset were used for external validation. The framework comprised a preprocessing step to segment the pulmonary vessels and the EmbNet to detect emboli. Emboli were divided into three location-based subgroups for detailed evaluation: central arteries, lobar branches, and peripheral regions. Ground truth was established by three radiologists.
RESULTS: The EmbNet\'s per-scan level sensitivity, specificity, positive predictive value (PPV), and negative predictive value were 90.9%, 75.4%, 48.4%, and 97.0% (internal validation) and 88.0%, 70.5%, 42.7%, and 95.9% (external validation). At the per-embolus level, the overall sensitivity and PPV of the EmbNet were 86.0% and 61.3% (internal validation), and 83.5% and 57.5% (external validation). The sensitivity and PPV of central emboli were 89.7% and 52.0% (internal validation), and 94.4% and 43.0% (external validation); of lobar emboli were 95.2% and 76.9% (internal validation), and 93.5% and 72.5% (external validation); and of peripheral emboli were 82.6% and 61.7% (internal validation), and 80.2% and 59.4% (external validation). The average false positive rate was 0.45 false emboli per scan (internal validation) and 0.69 false emboli per scan (external validation).
CONCLUSIONS: The EmbNet provides high sensitivity across embolus locations, suggesting its potential utility for initial screening in clinical practice.
METHODS: 305 consecutive CTPA scans between January 2019 and December 2021 were enrolled in this study (142 for training, 163 for internal validation), and 250 CTPA scans from a public dataset were used for external validation. The framework comprised a preprocessing step to segment the pulmonary vessels and the EmbNet to detect emboli. Emboli were divided into three location-based subgroups for detailed evaluation: central arteries, lobar branches, and peripheral regions. Ground truth was established by three radiologists.
RESULTS: The EmbNet\'s per-scan level sensitivity, specificity, positive predictive value (PPV), and negative predictive value were 90.9%, 75.4%, 48.4%, and 97.0% (internal validation) and 88.0%, 70.5%, 42.7%, and 95.9% (external validation). At the per-embolus level, the overall sensitivity and PPV of the EmbNet were 86.0% and 61.3% (internal validation), and 83.5% and 57.5% (external validation). The sensitivity and PPV of central emboli were 89.7% and 52.0% (internal validation), and 94.4% and 43.0% (external validation); of lobar emboli were 95.2% and 76.9% (internal validation), and 93.5% and 72.5% (external validation); and of peripheral emboli were 82.6% and 61.7% (internal validation), and 80.2% and 59.4% (external validation). The average false positive rate was 0.45 false emboli per scan (internal validation) and 0.69 false emboli per scan (external validation).
CONCLUSIONS: The EmbNet provides high sensitivity across embolus locations, suggesting its potential utility for initial screening in clinical practice.
摘要：
目的：提出一种卷积神经网络（EmbNet），用于在计算机断层扫描肺动脉造影（CTPA）扫描上自动检测肺栓塞，并评估其诊断性能。
方法：本研究纳入了2019年1月至2021年12月之间的305次连续CTPA扫描（142次用于培训，163用于内部验证)，来自公共数据集的250次CTPA扫描用于外部验证。该框架包括用于分割肺血管的预处理步骤和用于检测栓子的EmbNet。栓子分为三个基于位置的亚组进行详细评估：中央动脉，叶分支，和外围区域。真相是由三名放射科医生建立的。
结果：EmbNet的每扫描电平灵敏度，特异性，阳性预测值(PPV)，阴性预测值为90.9%，75.4%,48.4%,和97.0%(内部验证)和88.0%，70.5%,42.7%,和95.9%（外部验证）。在每个栓子水平上，EmbNet的总体灵敏度和PPV分别为86.0%和61.3%(内部验证)，83.5%和57.5%(外部验证)。中心栓塞的敏感性和PPV分别为89.7%和52.0%(内部验证),和94.4%和43.0%（外部验证）；叶栓子分别为95.2%和76.9%（内部验证），和93.5%和72.5%(外部验证);和周围栓塞的82.6%和61.7%(内部验证),80.2%和59.4%（外部验证）。平均假阳性率为0.45假栓塞/扫描(内部验证)和0.69假栓塞/扫描(外部验证)。
结论：EmbNet在栓子位置提供了高灵敏度，提示其在临床实践中初步筛查的潜在效用。
方法：本研究纳入了2019年1月至2021年12月之间的305次连续CTPA扫描（142次用于培训，163用于内部验证)，来自公共数据集的250次CTPA扫描用于外部验证。该框架包括用于分割肺血管的预处理步骤和用于检测栓子的EmbNet。栓子分为三个基于位置的亚组进行详细评估：中央动脉，叶分支，和外围区域。真相是由三名放射科医生建立的。
结果：EmbNet的每扫描电平灵敏度，特异性，阳性预测值(PPV)，阴性预测值为90.9%，75.4%,48.4%,和97.0%(内部验证)和88.0%，70.5%,42.7%,和95.9%（外部验证）。在每个栓子水平上，EmbNet的总体灵敏度和PPV分别为86.0%和61.3%(内部验证)，83.5%和57.5%(外部验证)。中心栓塞的敏感性和PPV分别为89.7%和52.0%(内部验证),和94.4%和43.0%（外部验证）；叶栓子分别为95.2%和76.9%（内部验证），和93.5%和72.5%(外部验证);和周围栓塞的82.6%和61.7%(内部验证),80.2%和59.4%（外部验证）。平均假阳性率为0.45假栓塞/扫描(内部验证)和0.69假栓塞/扫描(外部验证)。
结论：EmbNet在栓子位置提供了高灵敏度，提示其在临床实践中初步筛查的潜在效用。
