Naser Obeidat

Elastofibroma dorsi is a rare, benign fibroelastic tumor primarily affecting the subscapular region. Despite its characteristic imaging features, misdiagnosis is common, usually leading to unnecessary interventions. This study aims to assess the prevalence of elastofibroma dorsi among computed tomography (CT) scans of 2000 patients, to analyze the demographic and clinical characteristics of the patients and assess radiologists’ diagnostic accuracy regarding this condition. In this cross-sectional study a total of 2000 CT scans were retrospectively reviewed, scans positive for elastofibroma dorsi were then further analyzed for demographic and clinical features, including age, sex, comorbidities and lesions laterality. Additionally, 168 radiologists were assessed for their ability to accurately diagnose elastofibroma dorsi. A total of 45 patients has elastofibroma dorsi, representing a prevalence of 2.25%. The mean age of elastofibroma-patients was 73.49 ± 10.52 years, with a nearly equal sex distribution (48.89% male, 51.11% female). The most common disease or indication for the assessed CT images was malignancy (51.11%). Right sided involvement was predominant (86.67% of patients had right sided involvement whether alone or as part of bilateral involvement). Bilateral cases were 44.44%. Among the 168 radiologists, only 11.90% correctly identified elastofibroma dorsi on imaging. The most frequent misdiagnoses were mass or tumor (42.27%) and muscle (26.19%). Despite its distinct imaging characteristics, elastofibroma dorsi is frequently misdiagnosed, highlighting the need for increased awareness among radiologists. Due to its prevalence in elderly patients, elastofibroma dorsi should be included in the differential diagnosis of subscapular lesions to avoid misdiagnosis and unnecessary interventions.

The link between mesenteric panniculitis (MP) and cancer is debated. We explored this association after adjusting for age. We retrospectively analyzed abdominal CT scans of 8,750 patients between 2015 and 2017. Patients were categorized by cancer status. Of included patients, 8.7% had cancer. MP was found in 148 patients (58% males, mean age 57.8 years). MP was more frequent in cancer patients (5.14%) compared to no-cancer group (1.36%). Among MP patients, breast cancer was the most common malignancy. After adjusting for age, MP remained significantly associated with breast cancer and multiple myeloma in females and lymphoma in males. MP was significantly associated with breast cancer and multiple myeloma in females and lymphoma in males.

This systematic review aimed to explore the currently reported false positive findings of Gallium-68 (⁶⁸Ga)-1, 4, 7, 10-tetraazacyclododecane-1, 4, 7, 10-tetraacetic acid Tyr3-octreotide (⁶⁸Ga-DOTATOC). PubMed, Web of Science, and Scopus databases were used to conduct a systematic search and were updated until March 4, 2024. Three authors independently screened the titles and abstracts of the retrieved articles and selected the articles based on the inclusion and exclusion criteria. In a qualitative analysis of 42 included research articles involving 601 patients, 219 false positive findings were identified and categorized. Non-oncologic etiologies predominated, constituting 50.2% of pitfalls, followed by benign oncologic (27.4%) and malignant neoplasms (22.4%). Anatomically, the abdomen was the most common site for pitfalls (30.6%), followed by the musculoskeletal (22.4%), head and neck (20.5%), and pelvic (14.6%) regions. Chest region findings were least frequent, accounting for only 11.9%. This study elucidates potential false positive findings, predominantly occurring in the abdominal and head-neck regions-primary sites for meningiomas and neuroendocrine tumors (NETs). Understanding these false-positive findings is crucial for accurate diagnosis. Furthermore, recognizing these pitfalls may lead to novel applications of ⁶⁸Ga-DOTATOC beyond its conventional use in evaluating NETs and meningiomas, potentially expanding its current utility.

Objectives: Shoulder injury related to vaccine administration (SIRVA), previously observed with influenza vaccines, has gained clinical significance with widespread COVID-19 vaccination. However, few studies correlate vaccine types and demographic factors with the MRI findings of SIRVA. This study aimed to evaluate MRI findings of SIRVA following COVID-19 vaccination and assess associations with vaccine type and patient characteristics. Methods: A retrospective cohort study was conducted on 35 patients with new-onset shoulder complaints within six weeks of COVID-19 vaccination between May 2021 and May 2022. MRI findings suggestive of SIRVA were reviewed, including subacromial bursitis, rotator cuff tears, and adhesive capsulitis. Demographic data, vaccine type, clinical symptoms, and treatments were collected. Follow-up interviews (1–30 September 2024) assessed symptom persistence and vaccine hesitancy. Descriptive statistics and Chi-square tests were used to explore associations. Results: Of the 35 patients (mean age 53.6 ± 9.0 years; 54.3% female), subacromial bursitis was the most common MRI finding (89.5%), followed by tendonitis (47.4%) and adhesive capsulitis (36.8%). Tendonitis correlated with older age (p = 0.024) and AstraZeneca vaccination (p = 0.033). Subacromial bursitis was linked to female sex (p = 0.013) and higher BMI (p = 0.023). Adhesive capsulitis was associated with receiving the Sinopharm vaccine (p = 0.029). Persistent symptoms (22.9%) were more common in younger patients, women, and those with right-sided injections. Conclusions: SIRVA following COVID-19 vaccination showed different MRI patterns associated with female sex, higher BMI, and vaccine type. Awareness of these patterns may expedite recognition of COVID-19-associated SIRVA in routine practice.

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.