Computer-Aided Detection of Acute Pulmonary Embolism With 64-Slice Multi-Detector Row Computed Tomography: Impact of the Scanning Conditions and Overall Image Quality in the Detection of Peripheral Clots

Pixel-level annotated dataset of computed tomography angiography images of acute pulmonary embolism

Pulmonary embolism has a high incidence and mortality, especially if undiagnosed. The examination of choice for diagnosing the disease is computed tomography pulmonary angiography. As many factors can lead to misinterpretations and diagnostic errors, different groups are utilizing deep learning methods to help improve this process. The diagnostic accuracy of these methods tends to increase by augmenting the training dataset. Deep learning methods can potentially benefit from the use of images acquired with devices from different vendors. To the best of our knowledge, we have developed the first public dataset annotated at the pixel and image levels and the first pixel-level annotated dataset to contain examinations performed with equipment from Toshiba and GE. This dataset includes 40 examinations, half performed with each piece of equipment, representing samples from two medical services. We also included measurements related to the cardiac and circulatory consequences of pulmonary embolism. We encourage the use of this dataset to develop, evaluate and compare the performance of new AI algorithms designed to diagnose PE.

Computer-assisted detection of acute pulmonary embolism at CT pulmonary angiography in children and young adults: a diagnostic performance analysis

Background

To diagnose pulmonary embolism (PE) in children and adults since evaluating tiny pulmonary vasculature beyond segmental level is a challenging and demanding task with thousands of images.

Purpose

To evaluate the effect of computer-assisted detection (CAD) on acute PE on CTPA in children and young adults by readers with varying experience levels.

Material and Methods

Six radiologists were retrospectively divided into three groups according to experience levels and assessed the CTPA studies on a per-emboli basis. All readers identified independently the PE presence, and ranked diagnostic confidence on a 5-point scale with and without CAD. Reading time, sensitivities, specificities, accuracies, positive predictive values (PPVs), and negative predictive values (NPVs) were calculated for each reading.

Results

The sensitivities and NPVs differed significantly in most readers ( P = 0.004, 0.001, 0.010, 0.010, and 0.012 for sensitivities and P = 0.011, 0.003, 0.016, 0.017, and 0.019 for NPVs) except for reader 6 ( P = 0.148 and 0.165, respectively), and the accuracies of all readers differed significantly (all P < 0.05) in peripheral PE (beyond segmental level) detection readings with CAD versus without CAD between two reading methods. The overall time using CAD was longer than those without CAD (76.6 ± 54.4 s vs. 49.4 ± 17.7 s, P = 0.000) for all readers. Significant differences were found for confidence scores in inter-group measurements with CAD ( P = 0.045) and without CAD ( P < 0.001).

Conclusion

At the expense of longer reading time, the use of the CAD algorithms improves sensitivities, NPVs, and the accuracies of readers in peripheral PE detection, especially for readers with a poor level of interpretation experience.

Limitations of an automated embolism segmentation method in clinical practice

PURPOSE

Automated pulmonary embolism (PE) segmentation is frequently used as a preprocessing step in the quantitative analysis of pulmonary embolism. Objective of this study is to analyze the potential limitation in automated PE segmentation using clinical cases.

METHODS

A database of 304 computer tomography pulmonary angiography (CTPA) examinations was collected and confirmed to be PE. After processing using an automated scheme, two radiologists classified these cases into four groups of A, B, C and D, which represent 4 different segmentation results namely, (1) entire pulmonary artery identified without motivation artifacts, (2) entire pulmonary artery identified with motivation artifacts, (3) part of the pulmonary artery identified, and (4) none of the pulmonary artery identified. Then, the possible failed reasons in PE segmentation were analyzed and determined based on the image characterization of the diseases and the applied CTPA scanning protocols.

RESULTS

In the study, 143 (47.0%., 30 (9.9%., 110 (36.2%. and 21 (6.9%. examinations were classified into groups A, B, C and D, respectively. Group C and D included the cases with failed segmentation. Fifteen failure reasons, including intrapulmonary abnormalities, extra-pulmonary abnormalities, diffuse pulmonary diseases, enlarged heart, absolute occluded vessels, embolism attached to artery wall, delayed scan time, skewed location, low scan dose, obvious artifact of superior vena cava, previous chest surgery, congenital deformities of the chest, incorrect positioning, missed images and other unknown reasons, were determined with corresponding case percentages ranging from 0.3%.o 9.2%.

CONCLUSIONS

Automated segmentation failures were caused by specific lung diseases, anatomy varieties, improper scan time, improper scan dose, manual errors or other unknown reasons. Realization of those limitations is crucial for developing robust automated schemes to handle these issues in a single pass when a large number of CTPA examinations need to be analyzed.

Missed pulmonary emboli on CT angiography: assessment with pulmonary embolism-computer-aided detection

OBJECTIVE

The purpose of this study is to assess the use of a pulmonary embolism (PE)- computer-aided detection (CADx) program in the detection of PE missed in clinical practice.

MATERIALS AND METHODS

Pulmonary CT angiography (CTA) studies (n = 6769) performed between January 2009 and July 2012 were retrospectively assessed by a thoracic radiologist. In studies that were positive for PE, all prior contrast-enhanced pulmonary CTA studies were reviewed. Missed PE was deemed to have occurred if PE was not described in the final interpretation. The presence, proximal extent, and number of PEs were agreed on by three thoracic radiologists. Studies with missed acute PE and available slice thickness of 2 mm or less were assessed with a prototype PE-CADx program. False-positive PE-CADx marks were analyzed. Outcomes of missed acute PEs were assessed in patients with both follow-up imaging and clinical data.

RESULTS

Fifty-three studies with overlooked acute PE met our inclusion criteria for PE-CADx assessment. The PE-CADx program identified at least one PE in 77.4% of instances (41/53). PE-CADx correctly marked at least one PE in 23 of 23 cases (100%) with multiple PEs and 18 of 30 (60%) cases with a solitary PE (p < 0.001). PE-CADx per-study sensitivity was significantly higher for segmental (65.5%) than for subsegmental (91.7%) PEs (p = 0.002). PE-CADx averaged 3.8 false-positive marks per case (range, 0-23 marks). Fourteen patients with missed PE who were not receiving anticoagulation therapy developed new PEs, including nine with an isolated subsegmental PE on the initial CT scan.

CONCLUSION

PE-CADx correctly identified 77.4% of cases of acute PE that were previously missed in clinical practice.

Multiple lung parenchymal abnormalities: Don't panic, let's be pragmatic! The 6 question rule - a checklist strategy

Analysis of multiple lung parenchymal abnormalities on HRCT is a real diagnostic challenge. These abnormalities may be due to a disease of the pulmonary interstitial tissue, the bronchial tree, the cardiovascular system or to abnormal alveolar filling with fluid, blood, cells or tumor, several of these etiologies possibly being concomitant. Systematic pathophysiological reasoning, in the form of a logical checklist, guides reflection and covers many of the most frequent diagnoses and potentially treatable emergencies that can be identified by the non-specialist radiologist. This approach also provides a basis for deepening knowledge of each area. The use of the mnemonic FIBROVAKIM (fibrosis-bronchi-vascular-cancer-infection-medication) is easy to apply and summarizes this strategy.

Stand-alone performance of a computer-assisted detection prototype for detection of acute pulmonary embolism: a multi-institutional comparison

OBJECTIVE

To assess whether the performance of a computer-assisted detection (CAD) algorithm for acute pulmonary embolism (PE) differs in pulmonary CT angiographies acquired at various institutions.

METHODS

In this retrospective study, we included 40 consecutive scans with and 40 without PE from 3 institutions (n = 240) using 64-slice scanners made by different manufacturers (General Electric; Philips; Siemens). CAD markers were classified as true or false positive (FP) using independent evaluation by two readers and consultation of a third chest radiologist in discordant cases. Image quality parameters were subjectively scored using 4/5-point scales. Image noise and vascular enhancement were measured. Statistical analysis was done to correlate image quality of the three institutions with CAD stand-alone performance.

RESULTS

Patient groups were comparable with respect to age (p = 0.22), accompanying lung disease (p = 0.12) and inpatient/outpatient ratio (p = 0.67). The sensitivity was 100% (34/34), 97% (37/38) and 92% (33/36), and the specificity was 18% (8/44), 15% (6/41) and 13% (5/39). Neither significantly differed between the institutions (p = 0.21 and p = 0.820, respectively). The mean number of FP findings (4.5, 6.2 and 3.7) significantly varied (p = 0.02 and p = 0.03), but median numbers (2, 3 and 3) were comparable. Image quality parameters were significantly associated with the number of FP findings (p<0.05) but not with sensitivity. After correcting for noise and vascular enhancement, the number of FPs did not significantly differ between the three institutions (p = 0.43).

CONCLUSIONS

CAD stand-alone performance is independent of scanner type but strongly related to image quality and thus scanning protocols.