Background: Invasive sphenoid sinus aspergillosis is a rare but life-threatening condition usually found in immunocompromised patients. When involving cavernous sinus and surrounding structures, patients are frequently misdiagnosed with a neoplasm or sellar abscess. Timely diagnosis and intervention are crucial to patients' outcomes. The objective of this study is to review cases of invasive sphenoid sinus aspergillosis to describe disease manifestations, imaging features, treatment, and outcome. Case presentation: We describe four patients with invasive sphenoid sinus aspergillosis misdiagnosed as sellar tumors preoperatively. The mass was completely removed in three patients and partially removed in one patient microscopically. Pathological examinations confirmed Aspergillus in all cases. All four patients received anti-fungal agents postoperatively. There was no recurrence at the time of each patient's follow-up date. One patient with complete resection was lost to follow-up while the other three patients' neurologic function improved. Additionally, we performed a systematic review regarding invasive sphenoid sinus aspergillosis of existing English literature. Conclusion: With regard to clinical symptoms, headache, vision impairment, and ophthalmoplegia were observed in over half of the patients in the literature. A sellar mass with bone destruction on CT and involvement of cavernous sinus is highly suggestive of invasive fungal sphenoid sinusitis. Immediate surgical removal of the lesion is recommended for invasive sphenoid sinus aspergillosis to preserve nerve function and increase the likelihood of survival.
BackgroundIntracranial hemangiopericytoma/solitary fibrous tumor (SFT/HPC) is a rare type of neoplasm containing malignancies of infiltration, peritumoral edema, bleeding, or bone destruction. However, SFT/HPC has similar radiological characteristics as meningioma, which had different clinical managements and outcomes. This study aims to discriminate SFT/HPC and meningioma via deep learning approaches based on routine preoperative MRI.MethodsWe enrolled 236 patients with histopathological diagnosis of SFT/HPC (n = 144) and meningioma (n = 122) from 2010 to 2020 in Xiangya Hospital. Radiological features were extracted manually, and a radiological diagnostic model was applied for classification. And a deep learning pretrained model ResNet-50 was adapted to train T1-contrast images for predicting tumor class. Deep learning model attention mechanism was visualized by class activation maps.ResultsOur study reports that SFT/HPC was found to have more invasion to venous sinus (p = 0.001), more cystic components (p < 0.001), and more heterogeneous enhancement patterns (p < 0.001). Deep learning model achieved a high classification accuracy of 0.889 with receiver-operating characteristic curve area under the curve (AUC) of 0.91 in the validation set. Feature maps showed distinct clustering of SFT/HPC and meningioma in the training and test cohorts, respectively. And the attention of the deep learning model mainly focused on the tumor bulks that represented the solid texture features of both tumors for discrimination.
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