BIA-ALCL and BIA-SCC: Updates on Clinical Features and Genetic Mutations for Latest Recommendations.

Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) and Breast Implant-Associated Squamous Cell Carcinoma (BIA-SCC) are emerging neoplastic complications related to breast implants. While BIA-ALCL is often linked to macrotextured implants, current evidence does not suggest an implant-type association for BIA-SCC. Chronic inflammation and genetics have been hypothesized as key pathogenetic players, although for both conditions, the exact mechanisms and specific risks related to breast implants are yet to be established.

The FALD-V: A predictive formula for preoperative volume assessment of adipose tissue transplantation in FALD flap.

Background: The fat-augmented latissimus dorsi (FALD) flap is an evolution of the traditional latissimus dorsi (LD) flap, which allows to obtain a total autologous breast reconstruction (BR) avoiding the use of breast implants. The aim of this study was to develop a predictive preoperative formula in order to estimate and optimize the amount of fat to be transferred during FALD flap BR, using only anthropometric measurements.

Methods: We conducted a prospective clinical study between September 2020 and April 2023.

Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed.

The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55-69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays.

UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images.

Purpose: The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle.

Approach: Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations.