Incidental pulmonary embolism in patients with cancer: prevalence, underdiagnosis and evaluation of an AI algorithm for automatic detection of pulmonary embolism

Prospective Evaluation of Artificial Intelligence Triage of Incidental Pulmonary Emboli on Contrast-Enhanced CT Examinations of the Chest or Abdomen

Background: Artificial intelligence (AI) algorithms improved detection of incidental pulmonary embolism (IPE) on contrast-enhanced CT (CECT) examinations in retrospective studies; however, prospective validation studies are lacking. Objective: To assess the effect on radiologists' real-world diagnostic performance and report turnaround times of a radiology department's clinical implementation of an AI triage system for detecting IPE on CECT examinations of the chest or abdomen. Methods: This prospective single-center study included consecutive adult patients who underwent CECT of the chest or abdomen for reasons other than PE detection from May 12, 2021 to June 30, 2021 (phase 1) or from July 1, 2021 to September 29, 2021 (phase 2). Before phase 1, the radiology department installed a commercially available AI triage algorithm for IPE detection that automatically processed CT examinations and notified radiologists of positive results through an interactive floating widget. In phase 1, the widget was inactive, and radiologists interpreted examinations without AI assistance. In phase 2, the widget was activated, and radiologists interpreted examinations with AI assistance. A review process involving a panel of radiologists was implemented to establish the reference standard for the presence of IPE. Diagnostic performance and report turnaround times were compared using Pearson Chi-square test and Wilcoxon rank-sum test, respectively. Results: Phase 1 included 1467 examinations in 1434 patients (mean age, 53.8±18.5 years; 753 male, 681 female); phase 2 included 3182 examinations in 2886 patients (mean age, 55.4±18.2 years; 1520 male, 1366 female). The frequency of IPE was 1.4% (20/1467) in phase 1 and 1.6% (52/3182) in phase 2. Radiologists without AI, in comparison with radiologists with AI, showed significantly lower sensitivity (80.0% vs 96.2%, P=.03), without a significant difference in specificity (99.1% vs 99.9%, P=.58), for detection of IPE. The mean report turnaround time for IPE-positive examinations was not significantly different between radiologists without AI and radiologists with AI (78.3 vs 64.6 min, P=.26). Conclusion: An AI triage system improved radiologists' sensitivity for IPE detection on CECT examinations of the chest or abdomen without significant change in report turnaround times. Clinical Impact: This prospective real-world study supports the use of AI assistance for maximizing IPE detection.

[The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.

A real-world evaluation of the diagnostic accuracy of radiologists using positive predictive values verified from deep learning and natural language processing chest algorithms deployed retrospectively

Objectives

This diagnostic study assessed the accuracy of radiologists retrospectively, using the deep learning and natural language processing chest algorithms implemented in Clinical Review version 3.2 for: pneumothorax, rib fractures in digital chest X-ray radiographs (CXR); aortic aneurysm, pulmonary nodules, emphysema, and pulmonary embolism in CT images.

Methods

The study design was double-blind (artificial intelligence [AI] algorithms and humans), retrospective, non-interventional, and at a single NHS Trust. Adult patients (≥18 years old) scheduled for CXR and CT were invited to enroll as participants through an opt-out process. Reports and images were de-identified, processed retrospectively, and AI-flagged discrepant findings were assigned to two lead radiologists, each blinded to patient identifiers and original radiologist. The radiologist's findings for each clinical condition were tallied as a verified discrepancy (true positive) or not (false positive).

Results

The missed findings were: 0.02% rib fractures, 0.51% aortic aneurysm, 0.32% pulmonary nodules, 0.92% emphysema, and 0.28% pulmonary embolism. The positive predictive values (PPVs) were: pneumothorax (0%), rib fractures (5.6%), aortic dilatation (43.2%), pulmonary emphysema (46.0%), pulmonary embolus (11.5%), and pulmonary nodules (9.2%). The PPV for pneumothorax was nil owing to lack of available studies that were analysed for outpatient activity.

Conclusions

The number of missed findings was far less than generally predicted. The chest algorithms deployed retrospectively were a useful quality tool and AI augmented the radiologists' workflow.

Advances in knowledge

The diagnostic accuracy of our radiologists generated missed findings of 0.02% for rib fractures CXR, 0.51% for aortic dilatation, 0.32% for pulmonary nodule, 0.92% for pulmonary emphysema, and 0.28% for pulmonary embolism for CT studies, all retrospectively evaluated with AI used as a quality tool to flag potential missed findings. It is important to account for prevalence of these chest conditions in clinical context and use appropriate clinical thresholds for decision-making, not relying solely on AI.

Added value of an artificial intelligence algorithm in reducing the number of missed incidental acute pulmonary embolism in routine portal venous phase chest CT

OBJECTIVES

The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT).

METHODS

CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report. To determine the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, another experienced cardiothoracic radiologist with knowledge of the initial report and the AI output adjudicated.

RESULTS

The prevalence of acute incidental PE in the reference standard was 2.2% (67 of 3089 patients). In 25 cases, AI detected initially unreported PE. This included three cases concerning central/lobar PE. Sensitivity of the AI algorithm was significantly higher than the outcome of the initial report (respectively 95.5% vs. 62.7%, p < 0.001), whereas specificity was very high for both (respectively 99.6% vs 99.9%, p = 0.012). The AI algorithm only showed a slightly higher amount of false-positive findings (11 vs. 2), resulting in a significantly lower PPV (85.3% vs. 95.5%, p = 0.047).

CONCLUSION

The AI algorithm showed high diagnostic accuracy in diagnosing incidental PE, detecting an additional 25 cases of initially unreported PE, accounting for 37.3% of all positive cases.

CLINICAL RELEVANCE STATEMENT

Radiologist support from AI algorithms in daily practice can prevent missed incidental acute PE on routine chest CT, without a high burden of false-positive cases.

KEY POINTS

• Incidental pulmonary embolism is often missed by radiologists in non-diagnostic scans with suboptimal contrast opacification within the pulmonary trunk. • An artificial intelligence algorithm showed higher sensitivity detecting incidental pulmonary embolism on routine portal venous chest CT compared to the initial report. • Implementation of artificial intelligence support in routine daily practice will reduce the number of missed incidental pulmonary embolism.

Unreported incidental pulmonary embolism in patients with cancer: Radiologic natural history and risk of recurrent venous thromboembolism and death

PURPOSE

To assess the risk of recurrent venous thromboembolism (VTE) and death in patients with unreported cancer-associated incidental pulmonary embolism (iPE).

MATERIALS AND METHODS

Matched cohort study on cancer patients with a CT study including the chest between 2014-01-01 and 2019-06-30. Studies were reviewed for unreported iPE, and cases were matched with controls without iPE. Cases and controls were followed for one year, with recurrent VTE and death as outcome events.

RESULTS

Of the included 2960 patients, 171 patients had unreported and untreated iPE. While controls had a one-year VTE risk of 8.2 events per 100 person-years, cases with a single subsegmental iPE had a recurrent VTE risk of 20.9 events, and between 52.0 and 72.0 events per 100 person-years for multiple subsegmental iPE and more proximal iPE. In multivariable analysis, multiple subsegmental and more proximal iPE were significantly associated with the risk of recurrent VTE, while single subsegmental iPE was not associated with the risk of recurrent VTE (p = 0.13). In the subgroup of patients (n = 47) with cancer not in the highest Khorana VTE risk category, no metastases and up to three involved vessels, recurrent VTE occurred in two patients (4.7 cases per 100 person-years). There were no significant associations between iPE burden and risk of death.

CONCLUSION

In cancer patients with unreported iPE, iPE burden was associated with the risk of recurrent VTE. However, having a single subsegmental iPE was not associated with the risk of recurrent VTE. There were no significant associations between iPE burden and risk of death.

Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers

Artificial intelligence (AI) in medical care can raise diagnosis accuracy and improve its uniformity. This study developed a diagnostic imaging system for chronic wounds that can be used in medically underpopulated areas. The image identification algorithm searches for patterns and makes decisions based on information obtained from pixels rather than images. Images of 50 patients with pressure sores treated at Kobe University Hospital were examined. The algorithm determined the presence of necrosis with a significant difference (p = 3.39 × 10−5). A threshold value was created with a luminance difference of 50 for the group with necrosis of 5% or more black pixels. In the no-necrosis group with less than 5% black pixels, the threshold value was created with a brightness difference of 100. The “shallow wounds” were distributed below 100, whereas the “deep wounds” were distributed above 100. When the algorithm was applied to 24 images of 23 new cases, there was 100% agreement between the specialist and the algorithm regarding the presence of necrotic tissue and wound depth evaluation. The algorithm identifies the necrotic tissue and wound depth without requiring a large amount of data, making it suitable for application to future AI diagnosis systems for chronic wounds.

Perioperative Management of Incidental Pulmonary Embolisms on Trauma CT Scans: A Narrative Review

Unsuspected pulmonary embolism (PE) may be identified on an initial trauma computed tomography (CT) scan. The clinical importance of these incidental PEs remains to be elucidated. In patients who require surgery, careful management is needed. We sought to investigate the optimal perioperative management of such patients, including the use of pharmacological and mechanical thromboprophylaxis, possible thrombolytic therapy, and inferior vena cava (IVC) filters. A literature search was conducted, and all relevant articles were identified, investigated, and included. Medical guidelines were also consulted where appropriate. Pharmacological thromboprophylaxis is the mainstay of preoperative treatment, and low-molecular-weight heparins, fondaparinux, and unfractionated heparin may all be used. It has been suggested that prophylaxis should be administered as soon as possible after trauma. Such agents may be contraindicated in patients with significant bleeding, and mechanical prophylaxis and inferior vena cava filters may be favoured in these patients. Therapeutic anticoagulation and thrombolytic therapies may be considered but are associated with an increased risk of haemorrhage. Delaying surgery might help to minimise the risk of recurrent venous thromboembolism, and any interruption of prophylaxis must be strategically planned. Recommendations for postoperative care include a continuation of prophylaxis and therapeutic anticoagulation, with follow-up clinical evaluation within six months. Incidental PE is a common finding on trauma CT scans. Although the clinical significance is unknown, careful management of the balance between anticoagulation and bleeding is needed, especially in trauma patients and even more so in trauma patients requiring surgery.