Publications by authors named "A Beretta"
Background: Disease-related malnutrition, with or without inflammation, in older adults is currently emerging as a public health priority. The use of Foods for Special Medical Purposes, including Oral Nutritional Supplements, and supplements is crucial to support patients in achieving their nutritional needs. Therefore, this article aims to comprehensively provide an analysis of the adequacy of FSMPs in meeting the nutritional requirements of different age-related diseases and takes into account the emerging role of inflammation.
View Article and Find Full Text PDFBackground: Risk-stratification of patients with retroperitoneal sarcomas (RPS) relies on validated nomograms, such as Sarculator. This retrospective study investigated whether radiomic features extracted from computed tomography (CT) imaging could i) enhance the performance of Sarculator and ii) identify G3 dedifferentiated liposarcoma (DDLPS) or leiomyosarcoma (LMS), which are currently consider in a randomized clinical trial testing neoadjuvant chemotherapy.
Methods: Patients with primary localized RPS treated with curative-intent surgery (2011-2015) and available pre-operative CT imaging were included.
Article Synopsis
- Epithelioid hemangioendothelioma (EHE) is difficult to treat with traditional chemotherapy, prompting researchers to explore new therapies such as sirolimus and identify biomarkers for tumor aggressiveness.
- Scientists created a patient-derived xenograft (PDX) model from an advanced EHE patient to test sirolimus and to study serum levels of Growth/Differentiation Factor 15 (GDF-15) as a potential biomarker.
- The results indicated sirolimus was more effective than doxorubicin in reducing tumor growth and GDF-15 levels, establishing GDF-15 as a promising biomarker for EHE aggressiveness and potentially indicating the effectiveness of sirolimus in patients.
This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare.
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