Influenza vaccine related periostitis: A case report of a rare complication.

As social distancing guidelines continue to diminish across the country, viral pathogens that were once absent during the COVID-19 pandemic, such as influenza and RSV, have once again become prominent. Although serious side effects of vaccinations are rare, local complications of bursitis and skin and soft tissue infections are well-documented in the literature. We present a case of 1 such rare side effect: influenza vaccine related periostitis. A 39-year-old male patient presented with left shoulder pain which developed 2 days after an influenza vaccination administered to the left deltoid. His symptoms were persistent despite rest and 1 week trial of NSAIDs. MRI imaging demonstrated marrow edema and a periosteal reaction of the left shoulder. Overall, vaccine induced periostitis is poorly documented in the literature and the pathophysiology has not been fully characterized. Further research is crucial to identify patient specific risk factors and to raise awareness of this rare complication to promote swift diagnosis and effective treatment.

Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications.

Radiologists today play a central role in making diagnostic decisions and labeling images for training and benchmarking artificial intelligence (AI) algorithms. A key concern is low inter-reader reliability (IRR) seen between experts when interpreting challenging cases. While team-based decisions are known to outperform individual decisions, inter-personal biases often creep up in group interactions which limit nondominant participants from expressing true opinions. To overcome the dual problems of low consensus and interpersonal bias, we explored a solution modeled on bee swarms. Two separate cohorts, three board-certified radiologists, (cohort 1), and five radiology residents (cohort 2) collaborated on a digital swarm platform in real time and in a blinded fashion, grading meniscal lesions on knee MR exams. These consensus votes were benchmarked against clinical (arthroscopy) and radiological (senior-most radiologist) standards of reference using Cohen's kappa. The IRR of the consensus votes was then compared to the IRR of the majority and most confident votes of the two cohorts. IRR was also calculated for predictions from a meniscal lesion detecting AI algorithm. The attending cohort saw an improvement of 23% in IRR of swarm votes (k = 0.34) over majority vote (k = 0.11). Similar improvement of 23% in IRR (k = 0.25) in 3-resident swarm votes over majority vote (k = 0.02) was observed. The 5-resident swarm had an even higher improvement of 30% in IRR (k = 0.37) over majority vote (k = 0.07). The swarm consensus votes outperformed individual and majority vote decision in both the radiologists and resident cohorts. The attending and resident swarms also outperformed predictions from a state-of-the-art AI algorithm.

Spindle cell lesions of the breast: Multimodality imaging and clinical differentiation of pathologically similar neoplasms.

Spindle cell lesions of the breast comprise a wide-range of entities including reactive, benign and malignant proliferations. They can be pathologically challenging to differentiate as there is often immunohistochemical and morphologic similarities with characteristic spindle shaped cellular patterns. Radiological and pathological correlation is essential. Radiology detects, defines the size and extent, and assists in localizing the lesions. Pathology confirms the diagnosis and provides prognostic parameters. Familiarity with the clinicoradiological features of these diagnostically challenging lesions helps to establish an accurate pathological diagnosis and subsequent clinical decision making.