Peritoneal lymphomatosis, a rare presentation of lymphoma, can mimic peritoneal carcinomatosis. Computed tomography findings in both conditions include omental caking, as well as peritoneal enhancement, thickening and nodularity. We report three cases of peritoneal lymphomatosis and give an analysis of the salient imaging features that aided in the accurate diagnosis of the condition in each case. Two of the three cases demonstrated elevated serum cancer antigen 125 levels, a marker commonly associated with ovarian carcinoma. It is thus critical to distinguish peritoneal lymphomatosis from carcinomatosis, as the prognosis and management are dramatically different.
Adult ; Aged ; Aged, 80 and over ; CA-125 Antigen ; blood ; Carcinoma ; diagnosis ; diagnostic imaging ; Diagnosis, Differential ; Female ; Humans ; Lymphoma ; diagnosis ; diagnostic imaging ; Male ; Peritoneal Neoplasms ; diagnosis ; diagnostic imaging ; Peritoneum ; diagnostic imaging ; Tomography, X-Ray Computed

A 26-year-old male patient was referred for exercise-induced claudication that had interfered with his military duties for the past two years. He was an occasional smoker with no other significant cardiovascular risk factors. Initial Doppler ultrasonography showed narrowing of the popliteal artery. Further evaluation with magnetic resonance angiography demonstrated a short segment occlusion of the popliteal artery secondary to an anomalous origin of the medial head of the gastrocnemius muscle from the lateral femoral condyle. Based on the clinical presentation and imaging findings, he was diagnosed with popliteal artery entrapment syndrome. In this article, we discussed the typical presenting signs and symptoms of popliteal artery entrapment syndrome, the different imaging modalities available to aid diagnosis, classification of the condition and the available treatment options.

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.