Preoperative Arterial Embolization to Avoid Intraoperative Bleeding during Endoscopic Sinus Surgery for Organized Hematoma of the Maxillary Sinus: A Case Series and Literature Review

Sinonasal Organizing Hematoma: Demographics, Diagnosis, and Treatment Outcomes of 112 patients

INTRODUCTION

Sinonasal organizing hematoma (OH) is a rare, nonneoplastic lesion that often presents with epistaxis, a reddish mass, and destruction of the involved sinonasal structures. Due to its rarity, the demographics, diagnostic modalities, treatment strategies, and outcomes have not yet been studied in a large, long-term study.

MATERIALS AND METHODS

Retrospect cohort of 112 sinonasal OH patients treated between 1997 and 2020 in a tertiary, university hospital were evaluated. Demographics, systemic comorbidities, sinonasal surgery history, serum laboratory tests, radiological findings, and treatment results were collected. The present study aimed to assess the accuracy of preoperative computed tomography (CT), Gadolinum-enhanced magnetic resonance (MR), and punch biopsies in detecting sinonasal OH as the most likely diagnosis. In addition, incidental differences by age and year of diagnosis were calculated using the Poisson log-linear regression model.

RESULTS

The median age was 44, and 58% were male. Fewer than 20% of these cases had a chronic systemic comorbidity, bleeding tendency, or sinonasal surgery history. MR had the highest accuracy of (87%) to detect sinonasal OH as the most likely diagnosis, compared with contrast-enhanced-CT (53%), punch biopsy (49%), and non-enhanced-CT (16%) (all <0.05). Sinonasal OH incidence did not vary by age, but the yearly rate significantly increased by 1.05 times over 23 years (p < 0.05). Notably, 84% of 112 patients received surgical removal through the assistance of an endoscope, and none had substantial bleeding without preoperative embolization.

CONCLUSION

Sinonasal OH was observed regardless of age, sex, systemic comorbidities, bleeding tendency, prior sinonasal surgery, or trauma. Preoperative MR gives the highest accuracy for detecting this disease. Sinonasal OH may be safely managed with endoscopic-assisted surgery removal without embolization.

LEVEL OF EVIDENCE

4 Laryngoscope, 134:1581-1590, 2024.

Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network

OBJECTIVES

This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel.

METHODS

PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR).

RESULTS

The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%.

CONCLUSIONS

This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.