ABSTRACT
Subsequent to mass vaccination programs against COVID-19, diverse side effects have been described, both at the injection site, such as pain, redness and swelling, and systemic effects such as fatigue, headache, muscle or joint pain. On rare occasions, a lymphadenopathic syndrome may develop, raising the clinical suspicion of a lymphoproliferative disorder. We present the case of a 30-year-old woman who developed self-limiting left axillary lymphadenopathy following COVID-19 vaccination. To date, only seven similar cases with a complete clinicopathological description have been published, and fourteen cases have been notified to the European adverse events databases (Eudravigilance) in relationship with vaccination against COVID-19. It is important to be aware of this potential complication when a lymphadenopathic syndrome develops following vaccination, to avoid unnecessary treatment. (AU)
Tras la vacunación masiva frente a la COVID-19 se han comenzado a describir diversos efectos adversos incluyendo efectos locales en el lugar de la inyección, como dolor, enrojecimiento, hinchazón, etc., y efectos sistémicos como fatiga, dolor de cabeza, dolor muscular o articular. Más infrecuentemente se pueden desarrollar cuadros linfadenopáticos sospechosos clínicamente de proceso linfoproliferativo. Presentamos el caso de una mujer de 30 años que desarrolló linfadenopatía axilar izquierda tras la vacunación contra la COVID-19 con hallazgos histopatológicos de linfadenopatía necrotizante de tipo Kikuchi y resolución espontánea. Hasta el momento se han publicado 7 casos con descripción clinicopatológica completa en la literatura y notificado 14 casos en la Red Europea de Farmacovigilancia en relación con la vacunación. Es importante tener en cuenta esta entidad en linfadenopatías sospechosas de procesos linfoproliferativos en este contexto, para evitar un tratamiento innecesario. (AU)
Subject(s)
Humans , Female , Adult , Coronavirus Infections/epidemiology , Mass Vaccination/adverse effects , Histiocytic Necrotizing LymphadenitisABSTRACT
Subsequent to mass vaccination programs against COVID-19, diverse side effects have been described, both at the injection site, such as pain, redness and swelling, and systemic effects such as fatigue, headache, muscle or joint pain. On rare occasions, a lymphadenopathic syndrome may develop, raising the clinical suspicion of a lymphoproliferative disorder. We present the case of a 30-year-old woman who developed self-limiting left axillary lymphadenopathy following COVID-19 vaccination. To date, only seven similar cases with a complete clinicopathological description have been published, and fourteen cases have been notified to the European adverse events databases (Eudravigilance) in relationship with vaccination against COVID-19. It is important to be aware of this potential complication when a lymphadenopathic syndrome develops following vaccination, to avoid unnecessary treatment. (AU)
Tras la vacunación masiva frente a la COVID-19 se han comenzado a describir diversos efectos adversos incluyendo efectos locales en el lugar de la inyección, como dolor, enrojecimiento, hinchazón, etc., y efectos sistémicos como fatiga, dolor de cabeza, dolor muscular o articular. Más infrecuentemente se pueden desarrollar cuadros linfadenopáticos sospechosos clínicamente de proceso linfoproliferativo. Presentamos el caso de una mujer de 30 años que desarrolló linfadenopatía axilar izquierda tras la vacunación contra la COVID-19 con hallazgos histopatológicos de linfadenopatía necrotizante de tipo Kikuchi y resolución espontánea. Hasta el momento se han publicado 7 casos con descripción clinicopatológica completa en la literatura y notificado 14 casos en la Red Europea de Farmacovigilancia en relación con la vacunación. Es importante tener en cuenta esta entidad en linfadenopatías sospechosas de procesos linfoproliferativos en este contexto, para evitar un tratamiento innecesario. (AU)
Subject(s)
Humans , Female , Adult , Coronavirus Infections/epidemiology , Mass Vaccination/adverse effects , Histiocytic Necrotizing LymphadenitisABSTRACT
Subsequent to mass vaccination programs against COVID-19, diverse side effects have been described, both at the injection site, such as pain, redness and swelling, and systemic effects such as fatigue, headache, muscle or joint pain. On rare occasions, a lymphadenopathic syndrome may develop, raising the clinical suspicion of a lymphoproliferative disorder. We present the case of a 30-year-old woman who developed self-limiting left axillary lymphadenopathy following COVID-19 vaccination. To date, only seven similar cases with a complete clinicopathological description have been published, and fourteen cases have been notified to the European adverse events databases (Eudravigilance) in relationship with vaccination against COVID-19. It is important to be aware of this potential complication when a lymphadenopathic syndrome develops following vaccination, to avoid unnecessary treatment.
Subject(s)
COVID-19 Vaccines , COVID-19 , Histiocytic Necrotizing Lymphadenitis , Adult , Female , Humans , COVID-19/prevention & control , COVID-19 Vaccines/adverse effects , Dendritic Cells , Histiocytic Necrotizing Lymphadenitis/etiology , Lymph Nodes , VaccinationABSTRACT
BACKGROUND AND OBJECTIVES: Osteonecrosis of the femoral head (ONFH) is an incapacitating disease that frequently results in the collapse of the femoral head and secondary osteoarthritis. The diagnosis and staging of this pathology, which usually rely on imaging studies, are challenging. Currently, conventional radiography is the basis of the initial diagnostic assessment. In recent decades, however, radiographs have been considered insensitive to early changes in ONFH and thus, a suboptimal diagnostic tool. Paradoxically, the imaging features of radiographs are often profuse, substantial, and characteristic. This study aimed to elucidate the real limitations of this radiologic tool by assessing the diagnostic reliability of the key radiologic features and staging. METHODS: This was a retrospective study in which radiographs from 28 idiopathic ONFH confirmed cases who underwent hip arthroplasty were analyzed by eight observers who were asked to identify the presence or absence of ONFH universally reported imaging features in AP hip radiographs. RESULTS: Concordance analysis revealed a poor agreement between observers for most of the assessed imaging features. Only the identification of femoral head flattening and osteoarthritis signs exhibited moderate agreement with statistical significance. In contrast, the detection of radiological osteoporosis and the loss of trabeculation showed the lowest reliability, with negative kappa coefficients. CONCLUSION: There is a lack of agreement between qualified observers, even for the identification of the most characteristic ONFH radiographic feature. The reliability of plain radiography for the detection of basic radiological elements is even weaker in the early stages of the disease.
ABSTRACT
BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis. METHODS: We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model). RESULTS: Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively. CONCLUSIONS: The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.
