ABSTRACT
Fungal lung co-infections associated with COVID-19 may occur in severely ill patients or those with underlying co-morbidities, and immunosuppression. The most common invasive fungal infections are caused by aspergillosis, mucormycosis, pneumocystis, cryptococcus, and candida. Radiologists integrate the clinical disease features with the CT pattern-based approach and play a crucial role in identifying these co-infections in COVID-19 to assist clinicians to make a confident diagnosis, initiate treatment and prevent complications.
Subject(s)
COVID-19 , Coinfection , Mycoses , Pneumonia , Humans , COVID-19/complications , Coinfection/diagnostic imaging , Coinfection/complications , Mycoses/etiology , Mycoses/microbiology , Lung/diagnostic imaging , RadiologistsABSTRACT
Las coinfecciones pulmonares fúngicas asociadas a la COVID-19 pueden ocurrir en pacientes gravemente enfermos o con comorbilidades subyacentes e inmunosupresión. Las infecciones fúngicas invasivas más comunes son causadas por aspergilosis, mucormicosis, y las debidas a Pneumocystis, criptococo y cándida. Los radiólogos integran las características clínicas de la enfermedad con el enfoque basado en patrones de TAC y desempeñan un papel crucial en la identificación de estas coinfecciones en la COVID-19 para ayudar a los médicos a realizar un diagnóstico seguro, iniciar el tratamiento y prevenir complicaciones.(AU)
Fungal lung co-infections associated with COVID-19 may occur in severely ill patients or those with underlying co-morbidities, and immunosuppression. The most common invasive fungal infections are caused by aspergillosis, mucormycosis, pneumocystis, cryptococcus, and candida. Radiologists integrate the clinical disease features with the CT pattern-based approach and play a crucial role in identifying these co-infections in COVID-19 to assist clinicians to make a confident diagnosis, initiate treatment and prevent complications.(AU)
Subject(s)
Humans , Severe acute respiratory syndrome-related coronavirus , Coronavirus Infections , Betacoronavirus , Pandemics , Radiologists , Lung Diseases, Fungal , Pneumocystis , Cryptococcus , Candida , Aspergillosis , Radiology , Diagnostic Imaging , Radiology Department, HospitalABSTRACT
Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists should be aware of prior to implementing Artificial Intelligence in daily practice. Barriers include regulatory compliance, ethical issues, data privacy, cybersecurity, AI training bias, and safe integration of AI into routine practice. In this article, we summarize the issues and the impact on clinical radiology.
Subject(s)
Artificial Intelligence , Radiology , Humans , Privacy , RadiologistsABSTRACT
Fungal lung co-infections associated with COVID-19 may occur in severely ill patients or those with underlying co-morbidities, and immunosuppression. The most common invasive fungal infections are caused by aspergillosis, mucormycosis, pneumocystis, cryptococcus, and candida. Radiologists integrate the clinical disease features with the CT pattern-based approach and play a crucial role in identifying these co-infections in COVID-19 to assist clinicians to make a confident diagnosis, initiate treatment and prevent complications.
ABSTRACT
La inteligencia artificial (IA) ofrece la posibilidad de cambiar la práctica de la radiología clínica en todo el mundo. Sin embargo, existen dificultades que los radiólogos deben conocer antes de aplicar la inteligencia artificial en la práctica diaria. Estas dificultades incluyen cuestiones de cumplimiento de la legislación, cuestiones éticas, aspectos relacionados con la privacidad de los datos y la ciberseguridad, el sesgo de aprendizaje automático y la integración segura de la IA en la práctica habitual. En este artículo, resumimos estas cuestiones y su repercusión en la radiología clínica.(AU)
Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists should be aware of prior to implementing Artificial Intelligence in daily practice. Barriers include regulatory compliance, ethical issues, data privacy, cybersecurity, AI training bias, and safe integration of AI into routine practice. In this article, we summarize the issues and the impact on clinical radiology.(AU)
Subject(s)
Artificial Intelligence , Technology, Radiologic , Radiologists , Artificial Intelligence/ethics , Machine Learning , RadiologyABSTRACT
BACKGROUND AND PURPOSE: The NI-RADS lexicon doesn't use ADC parameters and T2 weighted signal for ascribing categories. We explored ADC, DWI, and T2WI to examine the diagnostic accuracy in primary sites of postsurgical oral cavity carcinoma in the Neck Imaging Reporting and Data System (NI-RADS) categories 2 and 3. MATERIALS AND METHODS: We performed a retrospective analysis in clinically asymptomatic post-surgically treated patients with oral cavity squamous cell carcinoma who underwent contrast-enhanced MRI between January 2013 and January 2016. Histopathology and follow-up imaging were used to ascertain the presence or absence of malignancy in subjects with "new enhancing lesions," which were interpreted according to the NI-RADS lexicon by experienced readers, including NI-RADS 2 and 3 lesions in the primary site. NI-RADS that included T2WI and DWI (referred to as NI-RADS A) and ADC (using the best cutoff from receiver operating characteristic curve analysis, NI-RADS B) was documented in an Excel sheet to up- or downgrade existing classic American College of Radiology NI-RADS and calculate diagnostic accuracy. RESULTS: Sixty-one malignant and 23 benign lesions included in the study were assigned American College of Radiology NI-RADS 2 (n = 33) and NI-RADS 3 (n = 51) categories. The recurrence rate was 90% (46/51) for NI-RADS three, 45% (15/33) for NI-RADS 2, and 73% (61/84) overall. T2WI signal morphology was intermediate in 45 subjects (53.5%) and restricted DWI in 54 (64.2%). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the American College of Radiology NI-RADS were the following: NI-RADS (75.4%, 78.3%, 90.1%, 54.5%, and 76.1%); NI-RADS A (79.1%, 81.2%, 91.9%, 59.1%, and 79.6%); and NI-RADS B (88.9%, 72.7%, 91.4%, 66.7%, and 85.1%), respectively. CONCLUSIONS: Adding MR imaging diagnostic characteristics like T2WI, DWI, and ADC to the American College of Radiology NI-RADS improved diagnostic accuracy and sensitivity.
Subject(s)
Head and Neck Neoplasms , Magnetic Resonance Imaging , Diffusion Magnetic Resonance Imaging/methods , Head and Neck Neoplasms/diagnostic imaging , Head and Neck Neoplasms/surgery , Humans , Magnetic Resonance Imaging/methods , Mouth , Retrospective Studies , Sensitivity and Specificity , Squamous Cell Carcinoma of Head and Neck/diagnostic imaging , Squamous Cell Carcinoma of Head and Neck/surgeryABSTRACT
Dopaminergic system in the prefrontal cortex (PFC) is known to regulate the cognitive functions. Catechol-O-methyl transferase (COMT), one of the major modulators of prefrontal dopamine function, has emerged as an important determinant of schizophrenia associated cognitive dysfunction and response to antipsychotics. A common Val->Met polymorphism (rs4680) in the COMT gene, associated with increased prefrontal dopamine catabolism, impairs prefrontal cognition and might increase risk for schizophrenia. Further, the degree of cognitive improvement observed with antipsychotics in schizophrenia patients is influenced by the COMT activity, and Val/Met has been proposed as a potential pharmacogenetic marker. However, studies evaluating the role of COMT have been equivocal. The presence of other functional polymorphisms in the gene, and the observed ethnic variations in the linkage disequilibrium structure at COMT locus, suggest that COMT activity regulation might be complex. Despite these lacunae in our current understanding, the influence of COMT on PFC mediated cognitive tasks is undeniable. COMT thus represents an attractive candidate for novel therapeutic interventions for cognitive dysfunction. The COMT activity inhibiting drugs including tolcapone and entacapone, have shown promising potential as they selectively modulate dopaminergic transmission. This review is an attempt to summarize the rapidly evolving literature exploring the diverse facets of COMT biology, its functional relevance as a predictive marker and a therapeutic target for schizophrenia.
