Qiao Yang, Gao Yaozong, Chen Yanbo, Ye Xiaodan, Yan Cheng, Zeng Mengsu
Department of Radiology, Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.
Shanghai Institute of Medical Imaging, Shanghai, China.
Quant Imaging Med Surg. 2025 Mar 3;15(3):1950-1962. doi: 10.21037/qims-24-1412. Epub 2025 Feb 26.
Computed tomography pulmonary angiography (CTPA) is the gold standard for the diagnosis of pulmonary embolism (PE). The semi-quantitative clot burden scoring based on imaging is related to the risk stratification and prognosis of acute PE, but it cannot be widely applied in the clinic due to the difficulty of calculation. This study developed a high-quality VB-Net deep learning (DL) model combined with Transformer, which can detect PE from images and automatically calculate the clot burden score (CBS). The aim of this study was to help patients via earlier diagnosis, risk stratification, and determination of treatment plans, thereby improving prognosis, as well as alleviate the burden on radiologists. To our knowledge, no related studies have been reported.
A retrospective inclusion of 2,424 CTPA examination cases (44% male) were conducted to train and test the VB-Net DL model for the detection of PE and to evaluate the clot burden volume and scoring. Area under the curve (AUC), and sensitivity and specificity on the case or clot level were used to evaluate the model's performance. Random CTPA data from Zhongshan Hospital Affiliated to Fudan University (30 cases with acute PE, 40 cases without PE) were applied to test the relationship between the clot burden automatically calculated by the model and the Qanadli score determined manually, as well as other imaging parameters.
The performance of the VB-Net DL model on the testing set had an AUC of 0.972 based on the case level. The sensitivity at the operational point of the model threshold selected was 94.6% [95% confidence interval (CI): 0.8650-0.9828], and the specificity was 89.4% (95% CI: 0.8407-0.9308). In the random CTPA examinations from this research center, the model's sensitivity based on the case was 76.67% (95% CI: 0.5880-0.8848), the specificity was 95.00% (95% CI: 0.8261-0.9950), the positive predictive value (PPV) was 92.00%, and the accuracy was 87.14%. On the clot-based level, the sensitivity was 84.43%, the PPV was 87.29%, and the false positive rate was 0.19 per case. The clot burden volume and score automatically measured by the model were significantly correlated with the manually determined Qanadli score (r=0.866, P<0.001 and r=0.899, P<0.001, respectively). The severity grading of the CBS groups was consistent with the degree of right ventricular dilation.
The VB-Net DL model based on CTPA could conveniently and efficiently detect and quantitatively evaluate PE.
计算机断层扫描肺动脉造影（CTPA）是诊断肺栓塞（PE）的金标准。基于影像的半定量血栓负荷评分与急性PE的危险分层及预后相关，但由于计算困难，无法在临床广泛应用。本研究开发了一种结合Transformer的高质量VB-Net深度学习（DL）模型，该模型可从图像中检测PE并自动计算血栓负荷评分（CBS）。本研究的目的是通过早期诊断、危险分层和确定治疗方案来帮助患者，从而改善预后，并减轻放射科医生的负担。据我们所知，尚未有相关研究报道。
回顾性纳入2424例CTPA检查病例（44%为男性），用于训练和测试VB-Net DL模型以检测PE并评估血栓负荷量及评分。采用曲线下面积（AUC）以及病例或血栓水平的敏感性和特异性来评估模型性能。应用复旦大学附属中山医院的随机CTPA数据（30例急性PE、40例无PE）来测试模型自动计算的血栓负荷与手动确定的Qanadli评分以及其他影像参数之间的关系。
VB-Net DL模型在测试集上基于病例水平的AUC为0.972。所选模型阈值操作点处的敏感性为94.6%[95%置信区间（CI）：0.8650 - 0.9828]，特异性为89.4%（95%CI：0.8407 - 0.9308）。在本研究中心的随机CTPA检查中，基于病例的模型敏感性为76.67%（95%CI：0.5880 - 0.8848），特异性为95.00%（95%CI：0.8261 - 0.9950），阳性预测值（PPV）为92.00%，准确性为87.14%。在基于血栓的水平上，敏感性为84.43%，PPV为87.29%，每例假阳性率为0.19。模型自动测量的血栓负荷量和评分与手动确定的Qanadli评分显著相关（分别为r = 0.866，P < 0.001和r = 0.899，P < 0.001）。CBS组的严重程度分级与右心室扩张程度一致。
基于CTPA的VB-Net DL模型能够方便、高效地检测并定量评估PE。
