Imaging Early Postoperative Complications of Cardiothoracic Surgery

Imaging findings of the postoperative chest: What the radiologist should know

Thoracic surgical procedures are increasing in recent years, and there are different types of lung resections. Postsurgical complications vary depending on the type of resection and the time elapsed, with imaging techniques being key in the postoperative follow-up. Multidisciplinary management of these patients throughout the perioperative period is essential to ensure an optimal surgical outcome. This pictorial review will review the different thoracic surgical techniques, normal postoperative findings and postsurgical complications.

Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network

Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016−01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48−99.38%) and 100.00% (95% CI 96.38−100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904−0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available.

The postoperative chest in lung cancer

Most of the complications following lung cancer surgery occur in the early postoperative period and can result in significant morbidity and mortality. Delayed complications can also occur. Diagnosing these complications can be challenging because clinical manifestations are non-specific. Imaging plays an important role in detecting these complications in a timely manner and facilitates prompt interventions. Hence, it is important to have knowledge of the expected anatomical alterations following lung cancer surgeries, and the spectrum of post-surgical complications and their respective imaging findings to avoid misinterpretations or delay in diagnosis.