Unique case of a post-traumatic nasoseptal arteriovenous malformation.

Extracranial manifestation of arteriovenous malformations (AVMs) is uncommon. Nasoseptal AVMs are an even rarer entity. In this case report, we present an interesting and first-of-its-kind case of the development of a left nasoseptal AVM in a 60-year-old man after a fall. This was likely post-traumatic, unlike the usual congenital AVMs described in the literature. The patient was managed conservatively with regular follow-up for the AVM as he was asymptomatic.

Ectopic sphenoid sinus pituitary adenoma masquerading as metastatic head and neck cancer.

A 68-year-old Chinese man was found to have a lobular mass in the sphenoid sinus which extended to the clivus and the roof of the nasopharynx on a staging MRI scan performed for his high-grade parotid salivary duct carcinoma. Further positron emission tomography scan showed that this lesion was fluorodeoxyglucose (FDG) avid. This proved to be a diagnostic dilemma. The patient underwent a total parotidectomy, left selective neck dissection and a transphenoidal biopsy of his nasal lesion. Final histology revealed that this lesion was a synchronous ectopic sphenoid sinus pituitary adenoma (ESSPA). Initial differential diagnoses that were considered included a chordoma, metastatic carcinoma and nasopharyngeal carcinoma. However, an important differential with a neoplastic appearance and a tendency for positive FDG uptake is an ESSPA. It requires dedicated immunohistochemical staining to diagnose, and its mainstay of treatment is surgical excision.

Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.

INTRODUCTION: We aimed to assess the attitudes and learner needs of radiology residents and faculty radiologists regarding artificial intelligence (AI) and machine learning (ML) in radiology. METHODS: A web-based questionnaire, designed using SurveyMonkey, was sent out to residents and faculty radiologists in all three radiology residency programmes in Singapore. The questionnaire comprised four sections and aimed to evaluate respondents' current experience, attempts at self-learning, perceptions of career prospects and expectations of an AI/ML curriculum in their residency programme. Respondents' anonymity was ensured. RESULTS: A total of 125 respondents (86 male, 39 female; 70 residents, 55 faculty radiologists) completed the questionnaire. The majority agreed that AI/ML will drastically change radiology practice (88.8%) and makes radiology more exciting (76.0%), and most would still choose to specialise in radiology if given a choice (80.0%). 64.8% viewed themselves as novices in their understanding of AI/ML, 76.0% planned to further advance their AI/ML knowledge and 67.2% were keen to get involved in an AI/ML research project. An overwhelming majority (84.8%) believed that AI/ML knowledge should be taught during residency, and most opined that this was as important as imaging physics and clinical skills/knowledge curricula (80.0% and 72.8%, respectively). More than half thought that their residency programme had not adequately implemented AI/ML teaching (59.2%). In subgroup analyses, male and tech-savvy respondents were more involved in AI/ML activities, leading to better technical understanding. CONCLUSION: A growing optimism towards radiology undergoing technological transformation and AI/ML implementation has led to a strong demand for an AI/ML curriculum in residency education.