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Abstract Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T2w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T1w, T2w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment.

Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious “black boxes”. The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes.

BACKGROUND: Antioxidant administration can significantly alleviate smoking-mediated oxidative stress. In our previous research, using a concentrated mixed juice powder led to significant reduction in oxidative status markers and improved lung function among smokers. OBJECTIVE: To compare the effectiveness of different antioxidant formulations of the retail product Juice Plus+® on oxidative status and lung function in healthy smokers via a a Bayesian Network (BN) analysis. METHODS: At baseline and after 3 months' supplementation, pulmonary function, cardiovascular risk factors, and oxidative status were assessed in 101 smokers, randomized into three groups: placebo (P), fruit/vegetable (FV), and fruit/vegetable/berry (FVB) group. BN analysis was performed via Monte Carlo simulations. Statistical significance was assigned as p < 0.05. RESULTS: No significant interaction was observed in P (network score –101.4, p = ns), whereas overall fit significantly improved in both FV (network score –185.7, p = 0.01) and FVB (–293.7, p = 0.003). Moreover, P group's respiratory parameters appeared unrelated to the biochemical structure, whereas those of the FV and, to a greater extent, FVB groups were definitely related to it CONCLUSIONS: BN analysis demonstrated that the FV and FVB formulations contain nutraceutical compounds capable of synergistically acting and improving antioxidant modulation and respiratory function in smoking subjects [ABSTRACT FROM AUTHOR]