Unsupervised clustering analysis-based characterization of spatial profiles of inaccuracy in apparent diffusion coefficient values with varying acquisition plan orientation and diffusion weighting gradient direction - a large multicenter phantom study.

This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis. A diffusion-weighted imaging (DWI) protocol (b-value: 0-200-400-600-800-1000 s mm) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function ().

Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.

Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.

Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model).

Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance.

Test-retest repeatability of myocardial radiomic features from quantitative cardiac magnetic resonance T1 and T2 mapping.

Radiomics of cardiac magnetic resonance (MR) imaging has proved to be potentially useful in the study of various myocardial diseases. Therefore, assessing the repeatability degree in radiomic features measurement is of fundamental importance. The aim of this study was to assess test-retest repeatability of myocardial radiomic features extracted from quantitative T1 and T2 maps.

Soft-Tissue Augmentation Around Dental Implants by a New Laser-Aided Pouch Roll Technique: Volumetric and Clinical Evaluation After 1 Year.

The ultimate goal in implantology is to restore the whole tooth-gingival complex in a fashion that cannot be distinguished from the rest of the natural dentition. This study assesses the volumetric and clinical changes in vestibular gingival soft tissues, crucial for satisfactory engraftment and esthetic results, upon treatment with laser-aided pouch roll augmentation in second-stage surgery for dental implant uncovering. Twelve patients with mild ridge deficiencies in 16 edentulous sites, including distal elements, were enrolled and reevaluated for up to 1 year.

Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance.

Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.

Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing-in terms of voxel size resampling, discretization, and filtering-on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy.

Keratin/Polylactic acid/graphene oxide composite nanofibers for drug delivery.

In this work keratin/poly(lactic acid) (PLA) 50/50 wt blend nanofibers with different loadings of graphene-oxide (GO) were prepared by electrospinning and tested as delivery systems of Rhodamine Blue (RhB), selected as a model of a drug. The effect of GO on the electrospinnability and drug release mechanism and kinetics was investigated. Rheological measurements carried out on the blend solutions revealed unsatisfactory compatibility between keratin and PLA under quiet condition.

Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy.

Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width).

Multi-photonic Adjunctive Therapy for the Management of Periodontitis: Recent Advances and New Treatment Approach.

The efficacy of photonic therapy adjunctive to conventional root cleansing procedures for the treatment of chronic periodontitis is matter of controversy. The meta-analyses of the clinical data available in the literature have failed to reach univocal conclusions because of broad variability among the applied photonic treatments, different in terms of light-emitting devices (laser or LED), wavelengths, irradiation power and modes, clinical indications, disease grading, follow-up times, and results assessment. Hovever, this complexity can also favour a different interpretation, which assigns a specific role to each photonic treatments in order to improve the outcome of the conventional treatments, in terms of reduction of periodontopathogenic bacteria and local inflammation, and increased regeneration of alveolar bone, periodontal ligament and gingiva.

Bioactive Keratin and Fibroin Nanoparticles: An Overview of Their Preparation Strategies.

In recent years, several studies have focused their attention on the preparation of biocompatible and biodegradable nanocarriers of potential interest in the biomedical field, ranging from drug delivery systems to imaging and diagnosis. In this regard, natural biomolecules-such as proteins-represent an attractive alternative to synthetic polymers or inorganic materials, thanks to their numerous advantages, such as biocompatibility, biodegradability, and low immunogenicity. Among the most interesting proteins, keratin extracted from wool and feathers, as well as fibroin extracted from cocoons, possess all of the abovementioned features required for biomedical applications.

Implementing Mindfulness in General Life and Organizations. Validation of the Time Flow Mindfulness Questionnaire for Effective Health Management.

The primary purpose of the current research was to examine the psychometric properties of the Time Flow Mindfulness Questionnaire (TFMQ), a new self-report scale designed to measure cognitive, emotional, bodily, context-related, and action-related distracting inputs experienced by the mind during three different time windows of mindfulness practice (preliminary moments, during-the-practice, after-the-practice). The 42-item scale assesses the following second-order and first-order factors: Practice (preliminary, during), Benefits (short-term, long-term) and Benefits at work. Three studies were conducted.

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy.

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification.

Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications.

Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging.

The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-order, GLCM, GLDM, GLRLM, GLSZM, and NGTDM). Radiomic features were estimated for 6 different isotropic resampling voxel sizes, using 10 interpolation algorithms (at fixed bin width) and 6 different bin widths (at fixed interpolation algorithm).

Silk Fibroin and Pomegranate By-Products to Develop Sustainable Active Pad for Food Packaging Applications.

In this study, a bio-based polymeric system loaded with fruit by-products was developed. It was based on silk fibroin produced by the silkworm and pomegranate peel powder, selected as active agent. The weight ratio between fibroin and pomegranate powder was 30:70.

Effect of data leakage in brain MRI classification using 2D convolutional neural networks.

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer's disease (AD) and Parkinson's disease (PD).

Efficacy of hemostatic powders as monotherapy or rescue therapy in gastrointestinal bleeding related to neoplastic or non-neoplastic lesions.

Background: Hemostatic powder (HP) in gastrointestinal bleeding (GIB) is mainly used as rescue therapy after failure of conventional hemostatic procedures (CHP).

Aim: To define the best field of application and the efficacy of HP as first choice monotherapy or rescue therapy.

Methods: We compared the efficacy of HP monotherapy, HP rescue therapy, and CHP in the management of active GIB due to neoplastic and non-neoplastic lesions.

On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis.

Purpose: The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation.

Methods: Water phantom acquisitions were carried out on 39 scanners.

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy.

Purpose: To perform a systematic review on the research on the application of artificial intelligence (AI) to imaging published in Italy and identify its fields of application, methods and results.

Materials And Methods: A Pubmed search was conducted using terms Artificial Intelligence, Machine Learning, Deep learning, imaging, and Italy as affiliation, excluding reviews and papers outside time interval 2015-2020. In a second phase, participants of the working group AI4MP on Artificial Intelligence of the Italian Association of Physics in Medicine (AIFM) searched for papers on AI in imaging.

Clinical care pathway program versus open-access system: a study on appropriateness, quality, and efficiency in the delivery of colonoscopy in the colorectal cancer.

Open-access colonoscopy (OAC), whereby the colonoscopy is performed without a prior office visit with a gastroenterologist, is affected by inappropriateness which leads to overprescription and reduced availability of the procedure in case of alarming symptoms. The clinical care pathway (CCP) is a healthcare management tool promoted by national health systems to organize work-up of various morbidities. Recently, we started a CCP dedicated to colorectal cancer (CRC), including a colonoscopy session for CRC diagnosis and prevention.

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study.

Nowadays, given the technological advance in CT imaging and increasing heterogeneity in characteristics of CT scanners, a number of CT scanners with different manufacturers/technologies are often installed in a hospital centre and used by various departments. In this phantom study, a comprehensive assessment of image quality of 5 scanners (from 3 manufacturers and with different models) for head CT imaging, as clinically used at a single hospital centre, was hence carried out. Helical and/or sequential acquisitions of the Catphan-504 phantom were performed, using the scanning protocols (CTDIvol range: 54.