Interstitial Lung Disease (ILD) refers to pulmonary disorders that affect the lung parenchyma through inflammation and fibrosis. It is possible to diagnose ILD visually with computed tomography (CT), but it is highly demanding. Machine learning (ML) has yielded powerful models, such as convolutional neural networks (CNN), that achieve state-of-the-art performance in image classification. However, even with advances in CNN explainability, an expert is often required to justify its decisions adequately. Radiomic features are more reada ble for medical analysis because they can be related to image characteristics and are intuitively used by radiologists. There is potential in using image data via CNN and radiomic features to classify lung CT images. In this work, we develop two ML models: a CNN for classifying ILD using CT scans; and a Multi-Layer Perceptron (MLP) for classifying healthy and ILD using radiomic features. In the ensemble approach, output weights of each model are combined, providing a robust method capable of classifying ILD with the CT and the radiomic features. From a high-resolution CT dataset with 32 x 32 patches of pathological lung and healthy tissues, we extract 92 radiomic features, excluding those above 90% Pearson correlation in the training sets of both cross -validation and final models. Using 0.6 for the MLP and 0.4 for the CNN as weights, our approach achieves an accuracy of 0.874, while the MLP achieved 0.870 and, the CNN.

High-resolution structural magnetic resonance imaging (MRI) allows neurological investigation, especially when brain volumes must be carefully delineated to monitor neurodegeneration, such as in multiple sclerosis (MS). This study compares different segmentation techniques applied to brain MRI to measure the white matter (WM) and grey matter (GM) in healthy and MS brains. We propose to evaluate the reliability and how each segmentation method could potentially affect clinical trials in MS. Four segmentation software were evaluated: Statistical Parametric Mapping (SPM), Lesion Segmentation Tool (LST), Freesurfer, and Siena/X. We simulated healthy and MS brain MRI and compared the segmentation volumes with the ground truth. Our results showed that LST provides overall good segmentation with low variability. When SienaX spatially normalizes the images, the WM and GM volumes are overestimated. On the other hand, Freesurfer underestimates volumes. We conclude that the use of different segmentation software produces variability in GM and WM volumes, especially in challenging situations, such as small lesions and in the presence of noise. The best method was the automatic region growth algorithm implemented using the LST, which uses T1-weighted and T2-FLAIR MRI.

Background:The Centiloid scale (CL) represents an attempt to standardize amyloid imaging to create a measure of global cortical β-amyloid deposition using positron emission tomography (PET). The CL approach transforms standard uptake value ratio (SUVR) into values ranging from 0 to 100. This study aims to investigate the brain amyloid load in SuperAgers (SA), healthy age-matched controls (ACs), and healthy middleaged controls (MCs).Method: Thirty cognitively healthy individuals were evaluated with a cognitive screening and the Rey Auditory-Verbal Learning Test (A7 list). They underwent an amyloid PET image with PIB were acquired in 3D mode from 40 to 60 minutes after injection using a PET/CT scanner (Discovery 600, GE Healthcare). Image analysis was performed using the PMOD software -PNEURO tool (PMOD Technologies). SUVR was transformed to CTX (Global Cortical Target region) using the whole cerebellum (WC) as reference. The equation used to convert SUVR to CL is CL=100 (SUVR-0.987)/1.019 and fits the parameters described previously.