European Radiology (2020) 30:3567–3575
https://doi.org/10.1007/s00330-020-06699-8
IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
Evaluation of acute pulmonary embolism and clot burden on CTPA
with deep learning
Weifang Liu 1,2 & Min Liu 2 & Xiaojuan Guo 3 & Peiyao Zhang 2 & Ling Zhang 2 & Rongguo Zhang 4 & Han Kang 4 &
Zhenguo Zhai 5 & Xincao Tao 5 & Jun Wan 5 & Sheng Xie 2
Received: 21 November 2019 / Revised: 3 January 2020 / Accepted: 31 January 2020 / Published online: 16 February 2020
# European Society of Radiology 2020
Abstract
Objectives To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism
(APE) on computed tomographic pulmonary angiography (CTPA).
Materials and methods The training set in this retrospective study consisted of 590 patients (460 with APE and 130 without APE) who
underwent CTPA. A fully deep learning convolutional neural network (DL-CNN), called U-Net, was trained for the segmentation of clot.
Additionally, an in-house validation set consisted of 288 patients (186 with APE and 102 without APE). In this study, we set different
probability thresholds to test the performance of U-Net for the clot detection and selected sensitivity, specificity, and area under the curve
(AUC) as the metrics of performance evaluation. Furthermore, we investigated the relationship between the clot burden assessed by the
Qanadli score, Mastora score, and other imaging parameters on CTPA and the clot burden calculated by the DL-CNN model.
Results There was no statistically significant difference in AUCs with the different probability thresholds. When the probability threshold
for segmentation was 0.1, the sensitivity and specificity of U-Net in detecting clot respectively were 94.6% and 76.5% while the AUC was
0.926 (95% CI 0.884–0.968). Moreover, this study displayed that the clot burden measured with U-Net was significantly correlated with
the Qanadli score (r = 0.819, p < 0.001), Mastora score (r = 0.874, p < 0.001), and right ventricular functional parameters on CTPA.
Conclusions DL-CNN achieved a high AUC for the detection of pulmonary emboli and can be applied to quantitatively calculate
the clot burden of APE patients, which may contribute to reducing the workloads of clinicians.
Key Points
• Deep learning can detect APE with a good performance and efficiently calculate the clot burden to reduce the physicians’
workload.
• Clot burden measured with deep learning highly correlates with Qanadli and Mastora scores of CTPA.
• Clot burden measured with deep learning correlates with parameters of right ventricular function on CTPA.
Keywords Neural networks (computer) . Pulmonary embolism . Computed tomography angiography . Lung
* Min Liu
drradiology@163.com
* Sheng Xie
xs_mri@126.com
1
Peking University Health Science Center, Beijing 100871, China
2
Department of Radiology, China-Japan Friendship Hospital, 2
Yinghua Dong Street, Hepingli, Chao Yang District, Beijing 100029,
China
3
Department of Radiology, Beijing Chaoyang Hospital of Capital
Medical University, Beijing 100019, China
4
Artificial Intelligence Scholar Center, Infervision, Beijing 100025,
China
5
Department of Pulmonary and Critical Care Medicine, China-Japan
Friendship Hospital, Beijing 100029, China
Abbreviations
Aod
Aorta diameter
APE
Acute pulmonary embolism
CTPA
Computed tomographic pulmonary angiography
DL
Deep learning
DL-CNN Deep learning convolutional neural
network
LAa
Area of the left atrium
LAd
Diameter of the left atrium
LVa
Area of the left ventricle
LVd
Diameter of the left ventricle
MPAd
Diameter of the main pulmonary artery
RAa
Area of the right and left atrium
RAd
Diameter of the right atrium
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RVa
RVd
Eur Radiol (2020) 30:3567–3575
Area of the right ventricle
Diameter of the right ventricle
Introduction
Acute pulmonary embolism (APE) is a common condition
that may lead to a high mortality rate if untreated with an
overall annual incidence of 8.7 per 100,000 people in China
[1]. Computed tomographic pulmonary angiography (CTPA)
has become the first-line choice for diagnosing and assessing
APE due to its non-invasion, wide availability, rapid acquisition, and interpretability [2]. Clot burden of APE was shown
to correlate with the short-term prognosis and might be useful
to evaluate treatment [3, 4]. Previously, quantifying clot burden in CTPA was presented by Mastora and Qanadli et al [5,
6]. However, their methods have not been widely used in
clinical practice since the calculation was time-consuming
and depended on the radiologists’ clinical experience.
With technical advances in computer science and the accumulation of medical data, deep learning (DL), as one type of
machine learning, has been increasingly used in medical imaging analysis [7–10]. In respiratory diseases, DL has been
successfully applied in the study of chronic obstructive pulmonary disease and lung nodules [11–13]. In terms of pulmonary embolism (PE), previous study [14] used a deep learning
convolutional neural network (DL-CNN) for natural language
processing to determine the presence of PE. However, this
work used radiology free-text reports, not CT scans. Another
study [15] applied a hypernetwork to assist pulmonary embolism diagnosis, but it used 28 clinical features without CT
findings. To our knowledge, the application of DL on detecting APE and calculating clot burden in CTPA has not been
reported. In this study, based on DL-CNN, we developed a
fully automatic algorithm, end-to-end fully convolutional network (i.e., U-Net) [16], to segment clot and calculate clot
volume in CTPA. This work aims to assess the performance
of U-Net to detect the clots in terms of efficiency and accuracy
as well as to calculate the clot burden of APE.
Materials and methods
Study population
This study was performed with approval from the Chinese
Clinical Trials Registry Center (http://www.chictr.org/en/;
Registration number ChiCTR-OCH-14004929) and was
approved by our institutional review board (Medical ethics
number: 2016-SSW-7). Informed consent was obtained from
all participants or their families. A CTPA dataset containing
both APE and without APE was retrospectively selected
according to clinical diagnosis. Diagnosis of APE was based
on the clinical diagnosis which was the guideline for the
prevention, diagnosis, and treatment of pulmonary
thromboembolism [17]. And the clinical diagnosis of APE
included both clinical information and CTPA findings.
Moreover, the exclusion criteria included the following: (1)
CTPA images with poor quality or severe motion artifact; (2)
patients who were clinically diagnosed with chronic pulmonary embolism, pulmonary arterial neoplasm, pulmonary vasculitis, and mediastinal fibrosis. Figure 1 demonstrates a flowchart detailing how participants were selected and research
underwent.
CT pulmonary angiography
CTPA was performed in the craniocaudal direction with multidetector CT scanners (Lightspeed VCT/64, GE Healthcare;
Toshiba Aquilion ONE TSX-301C/320; Philips iCT/256;
Siemens Sensation/16, SOMATOM Definition Dual Source
CT) by using a standard CT pulmonary angiography protocol.
The whole chest was craniocaudally scanned from lung apex
to the lowest hemidiaphragm during a single breath-hold.
Scan parameters were as follows: tube voltage of 100–
120 kV, tube current of 100–300 mAs, section thickness of
0.625–1 mm, table speed of 39.37 mm/s, gantry rotation time
of 0.8 s, and reconstruction increment of 1–1.25 mm. A soft
tissue reconstruction kernel was used. A mechanical injector
was used for intravenous bolus injection of iopromide
(Ultravist, 370 mg/ml, Bayer Schering Pharma) at a flow rate
of 5.0 ml/s. For optimal intraluminal contrast enhancement,
the automatic bolus-tracking technique had the region of interest positioned at the level of the main pulmonary artery with
a threshold of 100 HU predefined threshold, and a fixed delay
of 5 s was employed for data acquisition.
Training set
To protect the patients’ privacy, all CTPA images used in this
study were completely anonymized. A total of 590 cases
(F/M = 268/322, mean age = 52 ± 10 years) were retrospectively collected from two hospitals from Jan 2014 to
Dec 2015 for training model. The dataset included 460 patients with APE (F/M = 241/219, mean age = 55 ± 4 years)
and 130 patients without APE (F/M = 49/81, mean age = 43
± 7 years). To achieve a good segmentation performance, our
dataset was randomly split into a training dataset and a tuning
dataset. The training dataset (accounting for 80% of the
dataset), including 368 samples with APE (F/M = 181/187,
mean age = 53 ± 6 years) and 104 without APE (F/M = 41/
63, mean age = 46 ± 4 years), was used to train our segmentation model. Additionally, the tuning dataset (accounting for
20% of the dataset), comprising 92 samples with APE
(F/M = 38/54, mean age = 51 ± 8 years) and 26 without APE
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Eur Radiol (2020) 30:3567–3575
Fig. 1 A flowchart detailing of selecting participants
(F/M = 8/18, mean age = 40 ± 8 years), was used to select the
model parameters.
All CTPA images in the whole dataset were independently
delineated by two residents (Dr. W.F.L and Dr. X.J.G) with an
experience of 5 years using an open-source software platform,
i.e., 3D Slicer 4.8.0 (National Institutes of Health). All annotations followed the same protocol: clot located above (and
including) the sub-segment must be included. Our work was a
binary segmentation task (foreground vs. background). That
is, the clot regions, as foreground, were marked with “1,” and
otherwise, “0.” Finally, all annotations were reviewed and
adjusted by a 15-year experienced chest radiologist (Dr. S.X).
Workflow of the U-Net model
Figure 2 illustrates the overall workflow of our method which
mainly contains two steps: (1) clot segmentation, (2) clot volume calculation. In this study, U-Net was utilized to automatically perform the segmentation task and calculate the clot
volume.
Data preprocessing
The data preprocessing in this study included three steps.
First, all the CTPA images were processed using a window,
whose window width (WW) and window level (WL) respectively were 160–180 and 620–650. Then, all of pixel values in
the images were mapped to [0, 1] using the min-max normalization method. Finally, all the images were processed to form
three-channel images. Moreover, there was no clipping strategy adopted during the whole data preprocessing. Thus,
whether during training or inference, the input of the segmentation model was the images with a 512 × 512 pixel resolution.
Fully convolutional network for automatic
segmentation
To calculate the volume of pulmonary emboli, clot regions
were supposed to be extracted from images. Compared with
typical convolutional neural networks (CNNs), U-Net could
preserve local information and obtain precise results.
Therefore, we developed a DL model based on the U-Net
framework to automatically segment the clot regions from
CTPA images. The U-Net framework consisted of a
contracting path (the left part) and a symmetric expanding
path (the right part).On the contracting path, images are fed
through a series of convolution and downsampling operations.
On the expanding path, feature maps from contracting path are
combined via blocks of upsampling, concatenating, and
convolutional layers. In the U-Net, a 2 × 2 max pooling operation with stride 2 was used for downsampling, and correspondingly, a 2 × 2 transposed convolution layer with stride
2 was applied for upsampling. The “skip connection” procedure is applied for recovering spatial information loss caused
by max-pooling layers. All convolutional layers in U-Net
were 3 × 3 convolutions. ReLU activation function is used
for increasing non-linearity and batch normalization (BN)
was used to speed up the network convergence.
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Fig. 2 A complete workflow of clot segmentation network
Given a CTPA image from a patient, our trained model
would output the probability of each pixel for the foreground or the clots via the sigmoid function. To the best of
our knowledge, there is no research on the different probability thresholds for the clot detection. In this study, we set
the different probability thresholds (i.e., 0.1, 0.3, 0.5, 0.7,
and 0.99) to investigate the performance of U-Net for the
clot detection, and the final segmentation made by the model was obtained by a probability threshold. Generally, the
regions whose probability values were greater than the
threshold were considered the clots. Furthermore, using
clinical diagnosis as a standard, we calculated the sensitivity
and specificity of the U-Net model in the diagnosis of APE.
Then, the reproducibility of the U-Net model in detecting
APE was re-evaluated within 3 weeks.
Validation set
Additional 288 cases including 186 patients with APE
and 102 without APE in our hospital were collected as
an in-house validation set to assess the performance of
our model in the clot detection and clot volume calculation. CTPA was acquired from Jan 2016 to Oct 2018
(Toshiba Aquilion ONE TSX-301C/320; Philips iCT/
256; SOMATOM Definition Dual Source CT). For each
patient with APE, a volumetric assessment of clot burden was automatically provided by the clot segmentation mask of our model. The formula for the calculation
of the clot volume is shown as follows:
V ¼ ∑ w i hi T i
pi∈Ω
where Ω denotes the clot regions and pi denotes pixel i
of the clots. wi and hi respectively denote the width and
height of pi, and Ti denotes the thickness of CT slice
where pi is.
Clot burden and ventricular parameters on CTPA
Semi-quantitative clot burden was assessed with Mastora [5]
and Qanadli scores [6] by two independent 5-year residents
(Dr. P.Y.Z and Dr. L.Z). Other cardiovascular parameters on
CTPA were measured by the 12-year experienced radiologist
(Dr. M.L). Diameters of the main pulmonary artery (MPAd)
and aorta (Aod) and the septal angle [18] were measured on
axial CTPA images. Moreover, diameters (RVd and LVd) and
area (RVa and LVa) of the right and left ventricle and diameters
(RAd and LAd) and area (RAa and LAa) of the right and left
atrium were measured on a reconstructed four-chamber image. Then, we calculated the ratio of MPAd/Aod, RVd/LVd,
RVa/LVa, RAd/LAd, and RAa/ LAa, and meanwhile assessed
the presence or absence of pericardial effusion and pleural
effusion.
Statistical analysis
Statistical analysis was performed on SPSS (version 20.0,
IBM Corp.), GraphPad Prism (version 7.0a), and MedCalc
statistical software (version15.2). Generally, the MannWhitney U test was utilized for the comparison of quantitative
data while chi-square test for the comparison of qualitative
data. Correlations between continuous and categorical variables were measured by Spearman’s correlation tests.
Receiver operating characteristic (ROC) analysis, together
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with specificity and sensitivity, was performed to assess the
detection performance of the U-Net model for the clots of
APE. All of those tests were the two-sided test, and p < 0.05
was considered the level for statistical significance.
Results
Clinical information on the validation set
Of the 288 cases in the validation set, the average age
of 186 APE patients was 62 ± 16 years, including 100
females and 86 males. There were 102 patients without
APE including 60 females and 42 males, with an average age of 58 ± 14 years. Patients with APE were older
than patients without APE (U = 2900.500, p = 0.027),
while gender in the APE group was similar to that in
the group without APE (χ2 = 0.342, p = 0.559). Thirtyfour patients had pericardial effusion and 56 had pleural
effusion. Pericardial effusion (χ2 = 5.928, p = 0.015) and
pleural effusion (χ2 = 4.805, p = 0.028) were statistically
different between the two groups.
Performance of the U-Net model
Table 1 illustrates the segmentation results of the U-Net model
with different probability thresholds, and Fig. 3 shows the
corresponding ROC curve. The results indicated that there
was no statistically significant difference in AUCs with the
different probability thresholds for segmentation. Moreover,
the consistency of the two measurements was 100% using the
U-Net within 3 weeks.
Analysis of clot burden of APE
Figure 4 shows the detection for the clots and the calculation
of the clot burden. For APE patients, the median clot volume
measured by the U-Net was 2.04 ml (range, 0.02–33.81 ml),
2.30 ml (range, 0.02–33.66 ml), 2.27 ml (range, 0.02–
33.55 ml), 2.01 ml (range, 0.02–33.45 ml), and 1.97 ml
(range, 0.01–33.01 ml) respectively for the probability threshold of 0.1, 0.3, 0.5, 0.7, and 0.99.
Table 1 AUCs, sensitivity, and
specificity of U-Net at different
probability thresholds
Fig. 3 The ROC of the U-Net model with the probability threshold for
segmentation results setting as 0.1, 0.3, 0.5, 0.7, and 0.99
The median of Qanadli and Mastora scores in APE patients
respectively was 14 (range, 1–30) and 24 (range, 1–117).
Table 2 shows that there was a significant correlation between
the embolus volume calculated by the U-Net model and
Mastora score and Qanadli score, regardless of the probability
thresholds.
Additionally, for the 186 cases with APE of the validation
set, we did a comparative analysis of measuring time between
the U-Net model and the two manual methods. The results
showed that the average measuring time of the U-Net model
was 12.9 s (range, 9.1–16.7) for each sample, which was significantly less than that of two manual methods (Mastora
score 10.2 min, range 6.7–14.3; Qanadli score 9.8 min, range
4.2–11.0; p < 0.001).
Clot burden with other imaging parameters
Table 3 indicates the correlation coefficient between the clot
volume measured by the U-Net model with various probability thresholds and other imaging parameters on CTPA. Clot
volume positively correlated with RVa/LVa, RVd/LVd, RAa/
LAa, RAd/LAd, septal angle, and MPAd whatever the
The probability threshold
AUC
Sensitivity (%)
Specificity (%)
0.1
0.3
0.5
0.7
0.99
0.926 (95% CI 0.884–0.968)
0.925 (95% CI 0.883–0.968)
0.925 (95% CI 0.883–0.968)
0.925 (95% CI 0.882–0.968)
0.924 (95% CI 0.881–0.967)
94.60
93.50
93.50
93.50
93.50
76.50
76.50
76.50
76.50
76.50
AUC, Area under curve
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Fig. 4 The results of the trained
U-Net model. The black part inside the red line represents the clot
detection and clot burden
calculation
probability threshold was. However, there was no significant
correlation between the clot volume and MPAd/Aod. As
shown in Fig. 5, when the probability threshold was 0.1, the
clot volume in APE patients with pleural effusion was comparable with that in patients without pleural effusion, yet the
clot volume in APE patients with pericardial effusion was
higher than that in patients without pericardial effusion.
Moreover, the clot volume of APE patients with RVD/LVD
≥ 1 and MPAd ≥ 30 mm was higher than that of APE patients
with RVD/LVD < 1 and MPAd < 30 mm (probability threshold = 0.1).
Table 2 Relationship between clot volumes measured with U-Net and
two scores
Clot volume with DL-CNN Qanadli score Mastora score
Volume_0.1
Volume_0.3
Volume_0.5
Volume_0.7
Volume_0.99
R
p
R
p
0.819
0.798
0.799
0.822
0.825
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.874
0.851
0.873
0.876
0.878
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
Volume_0.1, the clot volume at setting value of 0.1; Volume_0.3, the clot
volume at setting value of 0.3; Volume_0.5, the clot volume at setting
value of 0.5; Volume_0.7, the clot volume at setting value of 0.7; Volume_
0.99, the clot volume at setting value of 0.99
Discussion
This is the first study to segment pulmonary emboli in
CTPA and automatically calculate the clot burden of APE
with the DL-CNN framework. For this study, there are
three key findings: (i) the DL-CNN model could achieve
a high performance (high sensitivity and AUCs) for the
reproducible detection of pulmonary emboli. (ii) The DLCNN could efficiently calculate the clot burden, and the
evaluated clot burden was highly correlated with Mastora
and Qanadli scores. (iii) The clot burden calculated by the
DL-CNN model was connected with the right heart function parameters on CTPA.
Identifying pulmonary emboli in CTPA is the basis for
diagnosing APE. In this study, we developed a deep framework based on U-Net to conduct pulmonary emboli segmentation. Since no studies reported the effect of probability
threshold setting on clot detection, we preset different probability thresholds for obtaining segmentation results and then
found that there was no statistically significant difference in
AUCs among different probability thresholds. Thus, we speculated the probability threshold for segmentation probably
might not affect the results, while it still needs to be validated
by further study.
In this research, whatever the probability threshold for segmentation results was set, the DL-CNN model always had a
high sensitivity and moderate specificity. This may be the
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Table 3
Relationship between clot volume and other parameters of CTPA
Clot volume vs.
RVa/LVa
RVd/LVd
RAd/LAd
RAa/LAa
Septal angle
MPAd
MPAd/Aod ratio
Volume_0.1
Volume_0.3
Volume_0.5
Volume_0.7
Volume_0.99
R
p
R
p
R
p
R
p
R
p
0.487
0.450
0.471
0.405
0.396
0.384
0.301
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.003
0.484
0.453
0.492
0.418
0.390
0.403
0.309
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.003
0.483
0.452
0.492
0.417
0.391
0.402
0.309
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.003
0.485
0.449
0.469
0.404
0.397
0.382
0.301
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.003
0.484
0.447
0.446
0.402
0.397
0.379
0.298
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.004
RVa/LVa, ratio of the right and left ventricular area; RVd/LVd, ratio of the right and left ventricular diameter; RAd/LAd, the ratio of the right and left atrial
diameter; RAa/LAa, the ratio of the right and left atrial area; MPAd, main pulmonary artery diameter; MPAd/Aod ratio, the ratio of the main pulmonary
artery diameter to aorta diameter
reason that (1) due to image artifact, similar to the tissue surrounding the vessel wall, and even effect of contrast agent, the
clot segmentation in CTPA using automatic DL-CNN algorithms was a challenging task. (2) There was no postprocessing in our study, such as dense conditional random
field, leading to small blood vessel edges being mistaken for
the clots. (3) Even though data were annotated by the domain
experts, label noise still could be a limiting factor in developing the model.
Rapid evaluation was necessary for the clot burden of APE,
since the clot burden was related to the prognosis of APE
patients [5, 19]. Moreover, quantification of clot burden was
usually used to assess the efficacy, potency, and optimal
Fig. 5 The correlation of clot
volume and pleural effusion,
pericardial effusion, RVd/LVd,
and MPAd. Pleural effusion(−):
patients without pleural effusion;
pleural effusion(+): patients with
pleural effusion; pericardial
effusion(−): patients without
pericardial effusion; pericardial
effusion(+): patients with
pericardial effusion; RVd/LVd(−):
RVd/LVd < 1; RVd/LVd(+): RVd/
LVd ≥ 1; MPAd(−): the main
pulmonary artery diameter
< 30 mm; MPAd(+): the main
pulmonary artery diameter
≥ 30 mm
duration of pharmaceuticals such as thrombolytics. Previous
studies mostly applied Qanadli and Mastora scores to semiquantitatively evaluate the clot burden of APE [5, 6, 20–22],
while we utilized DL method to automatically measure the
clot burden of APE patients. The results in the validation set
revealed that the clot burden measured by the automatic method was highly correlated with the traditional method (i.e.,
Qanadli and Mastora scores), and the consistency of 100%
with U-Net indicated the effectiveness and reliability of our
method. In addition, both Qanadli and Mastora scores required complex and cumbersome processes to assess the clot
burden, while our DL method only used a simple calculation
operation to evaluate the clot volume. Thus, U-Net was more
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efficient and convenient than semi-quantitative scores, which
might contribute to reducing the workloads of clinicians.
The parameters of right heart function are important to
determine the short-term prognosis of APE. Several reports
[23–25] had demonstrated that the RV/LV ratio based on
CTPA was a strong predictor of mortality. Some studies [21,
22, 25, 26] revealed that the Qanadli score and Mastora score
were related to right heart parameters. In the current study, clot
volume measured with DL-CNN also correlated with the parameters of the right heart function, such as RVa/LVa, RVd/
LVd, and the septal angle. These findings verified that the clot
burden of APE is an important factor affecting the right heart
function.
There were several limitations in our current study. First,
there was no enough large training set to train the DL-CNN
model, which might lead to overly optimistic segmentation
performance of the model. Second, all CTPA images including the training group were high-quality images without artifacts, which might limited the robustness of the model and
reduced the efficiency of clot detection in CTPA when dealing
with moderate or poor image quality. Third, we mainly investigated the potential relationship between the clot burden and
the imaging parameters but not the clinical parameters (e.g.,
myocardial enzymes, plasma D-dimer, and echocardiographic
data), and thus, we cannot analyze the correlation of the clot
burden with the clinical risk of APE. The last limitation was
only APE patients and patients without APE were included in
this research. Chronic pulmonary embolism, pulmonary tumor embolism, pulmonary artery sarcoma, pulmonary vasculitis, and fibrosing mediastinitis which mimic APE were excluded. Therefore, the current DL-CNN model is of limited
value in the differential diagnosis of APE with its mimics.
Further evaluations are needed to investigate the distribution
of pulmonary emboli with DL-CNN and the correlation of clot
burden with stratification of patient risk, which could help to
determine therapeutic options.
In conclusion, our study demonstrated that the DL-CNN
model could efficiently detect pulmonary emboli of APE and
automatically calculate the clot burden in CTPA, which had a
great potential to be a support tool for the therapeutic decision
in clinic.
Conflict of interest The authors of this manuscript declare no relationships with any companies, whose products or services may be related to
the subject matter of the article.
Funding information This work is supported by the National Natural
Science Foundation of China (81871328), Beijing Nature Science
Foundation (7182149), Youth Talents project of Chinese Academy of
Medical Science (2018RC320013), Beijing University of Chemical
Technology-China-Japan Friendship Hospital Research Project
(PYBA1807), and Beijing Science and Technology Commission
Pharmaceutical and Technology Innovation Project (Z181100001918034).
10.
Compliance with ethical standards
Guarantor The scientific guarantor of this publication is Prof. Min Liu,
MD.
Statistics and biometry No complex statistical methods were necessary
for this paper.
Informed consent Written informed consent was obtained from all patients or their family in this study.
Ethical approval Institutional Review Board approval was obtained
(medical ethics number: 2016-SSW-7).
Methodology
• Retrospective
• Diagnostic or prognostic study
• Multicenter study
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