Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

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Aging is a complex process that involves changes at both molecular and morphological levels. However, our understanding of how aging affects brain anatomy and function is still poor. In addition, numerous biomarkers and imaging markers, usually associated with neurodegenerative diseases such as Alzheimer's disease (AD), have been clinically used to study cognitive decline. However, the path of cognitive decline from healthy aging to a mild cognitive impairment (MCI) stage has been studied only marginally. This review presents aspects of cognitive decline assessment based on the imaging differences between individuals cognitively unimpaired and in the decline spectrum. Furthermore, we discuss the relationship between imaging markers and the change in their patterns with aging by using neuropsychological tests. Our goal is to delineate how aging has been studied by using medical imaging tools and further explore the aging brain and cognitive decline. We find no consensus among the biomarkers to assess the cognitive decline and its relationship with the cognitive decline trajectory. Brain glucose hypometabolism was found to be directly related to aging and indirectly to cognitive decline. We still need to understand how to quantify an expected hypometabolism during cognitive decline during aging. The Aβ burden should be longitudinally studied to achieve a better consensus on its association with changes in the brain and cognition decline with aging. There exists a lack of standardization of imaging markers that highlight the need for their further improvement. In conclusion, we argue that there is a lot to investigate and understand cognitive decline better and seek a window for a suitable and effective treatment strategy.

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A natureza estatística dos processos físicos envolvidos nos exames de medicina nuclear faz com que o uso do método de Monte Carlo (MC) seja uma ferramenta útil para cálculos da energia depositada e da dose absorvida nos órgãos, principalmente para avaliação de risco-benefício. O objetivo deste trabalho é avaliar as potencialidades e limitações do uso do aplicativo de simulação de Monte Carlo GATE (Geant4 Application for Emission Tomography) no cálculo da dosimetria interna em testes simulados de imagem de Medicina Nuclear. Foram comparados cálculos analíticos e simulações de fontes emissoras de radiação em fontes pontuais de 99mTc e 18F, em objetos atenuadores com geometrias simples. Foi realizada uma análise da influência do tamanho dos elementos do mapa de dose (dosel), assim como o impacto de diferentes configurações das fontes radioativas. Os resultados concordam com dados já publicados. Para uma simulação mais realística do 18F para fins de dosimetria, deve-se utilizar os dois tipos de configuração da fonte, “back-to-back”, que simula os fótons de aniquilação, e “Fluor18”, que simula o espectro de emissão de pósitrons. Conclui-se que o aplicativo GATE é um ambiente confiável e amigável para a estimativa de dose em imagens de medicina nuclear.

In lung cancer, early diagnosis can improve potentially the prognosis. Accurate interpretation of computed tomography (CT) scans demands significant efforts by radiologists due to the extensive number of slices analyzed in each examination, for each patient. Computer-aided diagnosis (CAD) systems have been applied in several medical fields, but mostly in lung nodules detection and classification. CAD systems for lung lesions classification usually extract different types of features from lesions, such as texture feature, shape and intensity. This exploratory study aims to investigate the performance of lung nodules classification in 2D and 3D CT lesions images using Haralick texture features analysis and binary logistic regression.  Expert radiologists manually segmented from a CT dataset of 17 benign and 20 malignant nodules, which have their anatomopathological results. Haralick features were extracted from 2D lesions images, using the largest cross-section nodule area, and from all nodule volume (3D). Principal Component Analysis (PCA) was applied to reduce texture features dimensionality, showing two and three principal components (PC) can explain 85.8% and 96.25% of data variance for 2D lesions, and 72.4% and 91.7% for 3D lesions, respectively. Binary logistic regression using leave-one-out cross-validation for training and test datasets showed no differences in accuracy (63% - 68%), using two or three PC. The higher sensitivity (75%) was acquired using 2D images with two or three PC, while the higher specificity (65%) was obtained using 3D images with two or three PC. Binary logistic regression using a small number of Haralick texture features showed better accuracy in lung nodules classification than visual evaluation by radiologists, although the limited dataset. Further studies are needed to generalize and improve these results.

In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w), using multivariate methods and artificial intelligence. We aimed to develop and validate a convolutional neural network (CNN) model for brain age prediction (PBA) using minimally processed T1w MRIs. Our model only requires one preprocessing step (i.e., image registration to MNI space), which is an advantage in comparison with previous methods that require more preprocessing steps. We used a multi-cohort dataset of cognitively healthy individuals comprising 16734 MRIs for training and evaluation. To validate our model and its interpretability, we used a multivariate model, Orthogonal Projections to Latent Structures (OPLS), which uses brain segmented cortical thicknesses and volumes. We trained and evaluated the models with the same dataset, and systematically investigated how predictions of the CNN model differ from those of the OPLS model. The validation of our model was made by testing an external dataset. The CNN and the OPLS model achieved a mean absolute error (MAE) in the testing dataset of 3.04 and 4.81 years, respectively. The model's performance in the external dataset was in the typical range of MAE found in the literature for testing sets. The CNN model revealed similar image patterns when grouped by chronological age (CA) and CNN predicted age. No significant differences were found between the oldest and youngest quartiles of age predictions by the CNN in a validation cohort of individuals with CA of 70 years old. Sensitivity maps analysis revealed that the age prediction is based mainly on the ventricles and other CSF spaces, which have been shown in the literature to reflect aging and are in accordance with the most important regions for the prediction in the OPLS model. While both the CNN and the OPLS model demonstrated acceptable performance metrics on a hold-out test set, individual predictions differed substantially, with brain age patterns of the CNN model being more comparable to the chronological age. In conclusion, our CNN model showed results comparable to the literature, using minimally processed images, which may facilitate the future implementation of brain age prediction in research and clinical settings.

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