Clot burden of acute pulmonary thromboembolism: comparison of two deep learning algorithms, Qanadli score, and Mastora score

Abstract

Background: The deep learning convolution neural network (DL-CNN) benefits evaluating clot burden of acute pulmonary thromboembolism (APE). Our objective was to compare the performance of the deep learning convolution neural network trained by the fine-tuning [DL-CNN (ft)] and the deep learning convolution neural network trained from the scratch [DL-CNN (fs)] in the quantitative assessment of APE. Methods: We included the data of 680 cases for training DL-CNN by DL-CNN (ft) and DL-CNN (fs), then retrospectively included 410 patients (137 patients with APE, 203 males, mean age 60.3±11.4 years) for testing the models. The distribution and volume of clots were respectively assessed by DL-CNN(ft) and DL-CNN(fs), and sensitivity, specificity, and area under the curve (AUC) were used to evaluate their performances in detecting clots on a per-patient and clot level. Radiologists evaluated the distribution of clots, Qanadli score, and Mastora score and right ventricular metrics, and the correlation of clot volumes with right ventricular metrics were analyzed with Spearman correlation analysis. Results: On a per-patient level, the two DL-CNN models had high sensitivities and moderate specificities (DL-CNN (ft): 100% and 77.29%; DL-CNN (fs): 100% and 75.82%), and their AUCs were comparable (Z=0.30, P=0.38). On a clot level, DL-CNN (ft) and DL-CNN (fs) sensitivities and specificities in detecting central clots were 99.06% and 72.61%, and 100% and 70.63%, respectively. DL-CNN (ft) sensitivities and specificities in detecting peripheral clots were mostly higher than those of DL-CNN (fs), and their AUCs were comparable. Clot volumes measured with the two models were similar (U=85094.500, P=0.741), and significantly correlated with Qanadli scores [DL-CNN(ft) r=0.825, P<0.001, DL-CNN(fs) r=0.827, P<0.001] and Mastora scores [DL-CNN(ft) r=0.859, P<0.001, DL-CNN(fs) r=0.864, P<0.001]. Clot volumes were also correlated with right ventricular metrics. Clot burdens were increased in the low-risk, moderate-risk, and high-risk patients. Binary logistic regression revealed that only the ratio of right ventricular area/left ventricular area (RVa/LVa) was an independent predictor of in-hospital death (odds ratio 6.73; 95% CI, 2.7-18.12, P<0.001). Conclusions: Both DL-CNN (ft) and DL-CNN (fs) have high sensitivities and moderate specificities in detecting clots associated with APE, and their performances are comparable. While clot burdens quantitatively calculated by the two DL-CNN models are correlated with right ventricular function and risk stratification, RVa/LVa is an independent prognostic factor of in-hospital death in patients with APE.

Development and validation of a lung graph–based machine learning model to predict acute pulmonary thromboembolism on chest noncontrast computed tomography

“…CTPA is the first-line method for detecting APE. In our previous study ( 19 - 21 ), DL based on CTPA was proven to be effective in clot detection and quantitative calculation of clot burden; however, compared with that of NC-CT, the radiation dose of CTPA is higher, and an iodine contrast agent is required. Despite this being the case, no study has yet confirmed whether NC-CT can be used in the diagnosis of APE, although some indirect signs such as subpleural wedge consolidation on NC-CT have been found to indicate APE ( 22 , 23 ).…”

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

“…Beyond the detection of PE, deep learning-based models to quantify clot burden are also being developed that have been shown to correlate with risk stratification markers in acute pulmonary embolism, including right ventricular metrics. 43,44 Similarly, ML-based tools have been developed for computer-aided diagnosis of DVT, although the majority utilize MR/CE-MRI or CT-venography, while the most widely employed diagnostic technique is compression ultrasound. [45][46][47][48] Aiming to equip non-specialists to detect DVT, a deep learning approach to compression ultrasound images was developed and externally validated with a negative predictive Bleeding, Thrombosis and Vascular Biology 2024; 3(s1):123 value of 98-99%.…”

Changes in Pulmonary Vascular Resistance and Obstruction Score Following Acute Pulmonary Embolism in Pigs

“…The investigator was not blinded to intervention, but timepoints for measurements were predetermined and CTPA scans were analyzed post hoc by a third part blinded to the interventions. CTPA scans were analyzed using a semi-quantitative method based on individual estimates; however, the method is well established and verified similar to other quantification scores ( 48 ).…”