Effective Dose Estimation in Computed Tomography by Machine Learning.

Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for radiological safety. The aim of this work is to estimate E from patient and CT acquisition parameters in the absence of a dose-tracking software exploiting machine learning.

A straightforward method for assessing the technical image quality of reconstructed and synthetic 2D images for Digital breast tomosynthesis systems.

Purpose: Digital Breast Tomosynthesis (DBT) is an advanced mammography technique for which there are currently no internationally agreed methods and reference values for image quality assessment. The aim of this multicentre study was to evaluate a simple method to assess the technical image quality of reconstructed and synthetic 2D (SM) images of different models of DBT systems using commercially available phantoms.

Methods: The signal difference to noise ratio (SDNR) was chosen as an index of technical image quality and was evaluated for three commercial phantoms, Tomophan, Tormam and CIRS model 015, on 55 DBT systems (six vendors, nine models).

Multiple Tumors in a Patient with Interleukin-2-Inducible T-Cell Kinase Deficiency: A Case Report.

Immune dysregulation in Inborn Errors of Immunity (IEI) shows a broad phenotype, including autoimmune disorders, benign lymphoproliferation, and malignancies, driven by an increasing number of implicated genes. Recent findings suggest that childhood cancer survivors (CCSs) may exhibit immunological abnormalities potentially linked to an underlying IEI, along with a well-known increased risk of subsequent malignancies due to prior cancer treatments. We describe a patient with two composite heterozygous pathogenic variants in the interleukin-2-inducible T-cell kinase () gene and a history of multiple tumors, including recurrent Epstein-Barr virus (EBV)-related nodular sclerosis and Hodgkin's lymphoma (NSHL), associated with unresponsive multiple hand warts, immune thrombocytopenia, and an impaired immunological profile (CD4+ lymphocytopenia, memory B-cell deficiency, reduction in regulatory T-cells, and B-cell- and T-cell-activated profiles).

Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?

Background: Breast cancer (BC) is the most common malignancy in women and the second cause of cancer death. In recent years, there has been a strong development in artificial intelligence (AI) applications in medical imaging for several tasks. Our aim was to evaluate the potential of transfer learning with convolutional neural networks (CNNs) in discriminating suspicious breast lesions on ultrasound images.

Moderate haemophilia A: Recommendations from a Spanish panel of experts.

Introduction: Diagnosing moderate haemophilia A (MHA) solely based on deficient FVIII protein levels limits its optimal management and delays the initiation of prophylaxis. Updating protocols and incorporating new variables into its diagnosis could prevent underestimating disease severity, avoiding early arthropathies and impairing patients' quality of life.

Aim: To propose recommendations to improve the comprehensive management of people with MHA.