Bilateral ossification of the auricles: an unusual entity and review of the literature

<p>Abstract</p> <p>Background</p> <p>True ossification of the auricle with cartilage replacement by bone, is a very rare clinical entity and can result in an entirely rigid auricle.</p> <p>Case presentation</p> <p>We present a rare case of bilateral ossification of the auricles in a 75-years old man with profound progressive rigidity of both auricles. His main complaint was a mild discomfort during resting making sleeping unpleasant without any other serious symptoms. His medical history was significant for predisposing factors for this condition such as, Addison's disease and diabetes mellitus. Excisional biopsy was performed confirming the ossified nature of the auricles. Further treatment deemed unnecessary in our case due to his mild clinical picture.</p> <p>Conclusion</p> <p>True auricular ossification is a quite rare clinical entity with unclear pathogenesis and one should have in mind that there is always the possibility of a serious co-existed disease like endocrinopathy.</p

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Multidetector computed tomography angiography: Application in vertebral artery dissection

<b>Background and Purpose:</b> Multidetector computed tomography angiography (MDCTA) is a minimally invasive radiological technique providing high-resolution images of the arterial wall and angiographic images of the lumen. We studied the radiological features of vertebral artery dissection (VAD) in a consecutive series of patients investigated for acute stroke and subarachnoid hemorrhage (SAH) in order to confirm and define the diagnostic features of VAD on MDCTA. <b>Patients and Methods:</b> Review of patients identified prospectively over a 4-year period with VAD assessed by MDCTA was conducted. Radiological features of VAD on MDCTA were reanalyzed utilising previously reported criteria for VAD. <b>Results:</b> Thirty-five patients (25 males, mean age 49.6 years) with a total of 45 dissected vertebral arteries were reviewed. MDCTA features of VAD included increased wall thickness in 44/45 (97.7&#x0025;) arteries and increased total vessel diameter in 42/45 arteries (93.3&#x0025;). All dissected arteries had either lumen stenosis (21/45) or associated segmental occlusion (24/45). An intimal flap was detected in 6/45 (13.3 &#x0025;) vessels. Twenty-five patients had follow-up imaging, 14/32 vessels returned to normal, 4 showed improvement in stenosis but did not return to normal and 14 demonstrated no change. The majority of non-occluded vessels became normal or displayed improved patency. Only 4/17 occluded arteries demonstrated re-establishment of flow. No adverse effects were recorded. <b>Conclusions:</b> MDCTA is a safe and reliable technique for the diagnosis of VAD. Increased wall thickness (97.7&#x0025;) and increased vessel wall diameter (93.3&#x0025;) were the most frequently observed features