A rare cause of unilateral leg swelling: May-Thurner Syndrome

Deep learning and radiomics-driven algorithm for automated identification of May-Thurner syndrome in Iliac CTV imaging

Objective

This research aimed to create a dataset of Iliac CTV scans for automated May-Thurner syndrome (MTS) detection using deep learning and radiomics. In addition, it sought to establish an automated segmentation model for Iliac Vein CTV scans and construct a radiomic signature for MTS diagnosis.

Methods

We collected a dataset of 490 cases meeting specific inclusion and exclusion criteria, anonymized to comply with HIPAA regulations. Iliac Vein CTV scans were prepared with contrast agent administration, followed by image acquisition and evaluation. A deep learning-based segmentation model, UPerNet, was employed using 10-fold cross-validation. Radiomic features were extracted from the scans and used to construct a diagnostic radiomic signature. Statistical analysis, including Dice values and ROC analysis, was conducted to evaluate segmentation and diagnostic performance.

Results

The dataset consisted of 201 positive cases of MTS and 289 negative cases. The UPerNet segmentation model exhibited remarkable accuracy in identifying MTS regions. A Dice coefficient of 0.925 (95% confidence interval: 0.875-0.961) was observed, indicating the precision and reliability of our segmentation model. Radiomic analysis produced a diagnostic radiomic signature with significant clinical potential. ROC analysis demonstrated promising results, underscoring the efficacy of the developed model in distinguishing MTS cases. The radiomic signature demonstrated strong diagnostic capabilities for MTS. Within the training dataset, it attained a notable area under the curve (AUC) of 0.891, with a 95% confidence interval ranging from 0.825 to 0.956, showcasing its effectiveness. This diagnostic capability extended to the validation dataset, where the AUC remained strong at 0.892 (95% confidence interval: 0.793-0.991). These results highlight the accuracy of our segmentation model and the diagnostic value of our radiomic signature in identifying MTS cases.

Conclusion

This study presents a comprehensive approach to automate MTS detection from Iliac CTV scans, combining deep learning and radiomics. The results suggest the potential clinical utility of the developed model in diagnosing MTS, offering a non-invasive and efficient alternative to traditional methods.

May-Thurner Syndrome: The Worst-Case Scenario

May-Thurner syndrome (MTS) is caused by compression of the left common iliac vein by the right common iliac artery against the spinal column. It can range from asymptomatic or present with subtle and unspecific signs and symptoms and rarely exhibit severe complications such as pulmonary embolism (PE). The diagnosis is confirmed by typical imaging findings. Treatment may include conservative measures, anticoagulation, endovascular or even surgical options. We report the case of a 20-year-old female who presented with cardiac arrest caused by an acute massive PE. Further study showed partial thrombosis of the internal iliac veins resulting from MTS. She continued anticoagulation therapy with low-molecular-weight heparin and then switched to edoxaban with a good clinical outcome. She was also referred to Vascular Surgery to discuss the possibility of iliac vein stenting. Abdominopelvic vascular compression syndromes include a large spectrum of conditions, and they are rarely considered as an etiology for venous thromboembolism. The clinical presentation of PE varies with several triggering factors and atypical presentation is more common in nonmalignant causes. The combination of noninvasive and invasive imaging modalities might be beneficial to establish a definitive diagnosis. Nevertheless, invasive procedures are often restricted to doubtful cases or to guide endovascular procedures which is the current treatment of choice. There is little evidence using nonvitamin K oral anticoagulants, but there are some case reports detailing their successful use. This case aims to point out the need for a profound understanding of different causes of deep vein and pulmonary thromboembolism; common entities in our practice but with a variety of clinical presentations and potentially caused by rare underlying conditions. MTS can be the origin of serious and deadly complications, hence the importance of early recognition and treatment.