Background: Artificial intelligence (AI) in radiology has improved diagnostic performance and shortened reading times of coronavirus disease 2019 (COVID-19) patients' studies. Objectives: The objectives pf the study were to analyze the performance of a chest computed tomography (CT) AI quantitative algorithm for determining the risk of mortality/mechanical ventilation (MV) in hospitalized COVID-19 patients and explore a prognostic multivariate model in a tertiary-care center in Mexico City. Methods: Chest CT images of 166 COVID-19 patients hospitalized from April 1 to 20, 2020, were retrospectively analyzed using AI algorithm software. Data were collected from their medical records. We analyzed the diagnostic yield of the relevant CT variables using the area under the ROC curve (area under the curve [AUC]). Optimal thresholds were obtained using the Youden index. We proposed a predictive logistic model for each outcome based on CT AI measures and predetermined laboratory and clinical characteristics. Results: The highest diagnostic yield of the assessed CT variables for mortality was the percentage of total opacity (threshold >51%; AUC = 0.88, sensitivity = 74%, and specificity = 91%). The AUC of the CT severity score (threshold > 12.5) was 0.88 for MV (sensitivity = 65% and specificity = 92%). The proposed prognostic models include the percentage of opacity and lactate dehydrogenase level for mortality and troponin I and CT severity score for MV requirement. Conclusion: The AI-calculated CT severity score and total opacity percentage showed good diagnostic accuracy for mortality and met MV criteria. The proposed prognostic models using biochemical variables and imaging data measured by AI on chest CT showed good risk classification in our population of hospitalized COVID-19 patients. (REV INVEST CLIN. [AHEAD OF PRINT])
Objective
The aim of the study was to evaluate the 18F-PSMA-1007 PET/computed tomography (CT) semiautomatic volumetric parameters to assess the whole-body tumor burden and its correlation with prostate-specific antigen (PSA) and Gleason score in patients with biochemically recurrent prostate cancer (PCa).
Materials and methods
A total of 110 patients referred for 18F-PSMA-1007 PET/CT due to biochemical recurrence were retrospectively analyzed. Whole-body total lesion prostate-specific membrane antigen (wbTl-PSMA) and whole-body PSMA-derived tumor volume (wbPSMA-TV) metrics on 18F-PSMA-1007 were obtained semiautomatically in dedicated software. A Spearman test was performed to explore the correlation of volumetric imaging parameters with PSA levels and Gleason score. To analyze the association between volumetric measures and PSA subgroups, we used a Kruskal–Wallis test and a Dunn’s test to identify each group causing an observed difference.
Results
A total of 492 metastatic lesions were analyzed, and a significant correlation was found between wbTL-PSMA (R = 0.63, P < 0.0001) and wbPSMA-TV (R = 0.49, P < 0.0001) with serum PSA. A statistically significant difference with wbTL-PSMA was found in patients with a PSA less than or equal 0.5 ng/ml and PSA in the range of 0.51–1.0 ng/ml.
Conclusion
18F-PSMA-1007 PSMA volumetric parameters can provide a quantitative imaging biomarker for whole-body tumor burden.
AD is an emergency in which diagnosis and timely management are essential to improve prognosis. In the sample presented here, a significant association was found in patients with a history of Marfan syndrome and abdominal aneurysms with dissections according to the Stanford classification. The rest of the independent variables did not show any significant association, probably related to the size of the sample.
Objetivos: Establecer la precisión diagnóstica por tomografía computarizada (TC) de la probabilidad de neumopatía por enfermedad por coronavirus 2019 , dada por el sistema de inteligencia artificial (IA) diseñado por Siemens, y el resultado de la evaluación cualitativa Reporting and Data System) con el estándar de referencia reacción en cadena de la polimerasa transcriptasa inversa (RT-PCR), entregando así la experiencia de nuestra institución. Métodos: Se realizó un estudio observacional, comparativo y retrolectivo en 192 pacientes adultos con sospecha de infección por coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) que contaban con prueba PCR. Se obtuvo la información de precisión diagnóstica luego de comparar el estándar de referencia (RT-PCR) con el CO-RADS realizado por los observadores y la probabilidad de COVID-19 que arrojaron las imágenes de TC mediante la IA. Resultados: La comparación de la probabilidad de COVID-19 obtenida por la IA vs. la RT-PCR para SARS-CoV-2 generó un AUC ROC de 0.774 (IC: 0.69-0.81) con p = 0.0001. La probabilidad de COVID-19 tuvo una precisión aceptable, con un buen valor predictivo positivo del 87.80%, pero con un pobre valor predictivo negativo del 58.80%. La variable CO-RADS vs. PCR obtuvo una mayor precisión con valores de sensibilidad y especificidad del 91.80 y 88.7% respectivamente. Conclusión: La comparación entre los resultados obtenidos por la IA y por la variable CO-RADS mostró mayor efectividad en esta última, sin embargo se logró documentar el alto impacto que tiene el sistema de cuantificación automática en la evaluación de estos pacientes, ya que permite agilizar la valoración del radiólogo y funciona como complemento en casos de dudas diagnósticas.
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