Impact assessment support study for the review of the Community guidelines on State aid for railway undertakings

In the wake of stagnant modal share of the European rail freight sector, this support study provides market information for the revision of the Guidelines on State aid for railway undertakings. It addresses four areas of interest: status of rail infrastructure; accessibility and costs pertaining to rolling stock; profitability and demand elasticity of rail freight ser- vices; and effectiveness of State support measures. A novel dataset of costs and revenues of rail freight across Europe, compiled using both publicly available data and input from extensive stakeholder consultation was built for that purpose. The findings are as follows. The inadequacy of intermodal terminals, congested rail networks, and costliness of private sidings all restrict the capacity of European rail infrastructure. Access to rolling stock is characterised by high costs and a lack of technical standardisation across Member States. We find that rail freight sectors in many countries are loss-making, with some segments being profitable. Efficient transshipment and transport of high freight volumes over long distances improves profitability of intermodal operations. The study shows that price sen- sitivity of rail freight services differs depending on the level of competition faced by road transport. The study also highlights to what extent higher thresholds for proportionate State aid and improved flexibility of schemes could be considered

Quality of residential facilities in Italy: satisfaction and quality of life of residents with schizophrenia spectrum~disorders

Background Recovery and human rights promotion for people with Schizophrenia Spectrum Disorders (SSDs) is fundamental to provide good care in Residential Facilities (RFs). However, there is a concern about rehabilitation ethos in RFs. This study aimed to investigate the care quality of Italian RFs, the quality of life (QoL) and care experience of residents with SSD. Methods Fourty-eight RFs were assessed using a quality assessment tool (QuIRC-SA) and 161 residents with SSD were enrolled. Seventeen RFs provided high intensity rehabilitation (SRP1), 15 medium intensity (SRP2), and 16 medium-low level support (SRP3). Staff-rated tools measured psychiatric symptoms and psychosocial functioning; user-rated tools assessed QoL and satisfaction with services. RFs comparisons were made using ANOVA and Chi-squared. Results Over two-thirds patients (41.5 y.o., SD 9.7) were male. Seventy-six were recruited from SRP1 services, 48 from SRP2, and 27 from SRP3. The lowest QuIRC-SA scoring was Recovery Based Practice (45.8%), and the highest was promotion of Human Rights (58.4%). SRP2 had the lowest QuIRC-SA ratings and SRP3 the highest. Residents had similar psychopathology (p = 0.140) and functioning (p = 0.537). SRP3 residents were more employed (18.9%) than SRP1 (7.9%) or SRP2 (2.2%) ones, and had less severe negative symptoms (p = 0.016) and better QoL (p = 0.020) than SRP2 residents. There were no differences in the RF therapeutic milieu and their satisfaction with care. Conclusions Residents of the lowest supported RFs in Italy had less severe negative symptoms, better QoL and more employment than others. The lowest ratings for Recovery Based Practice across all RFs suggest more work is needed to improve recovery

Repeatability and reproducibility of MRI-radiomic features: A phantom experiment on a 1.5 T scanner

Purpose Aim of this study is to assess the repeatability of radiomic features on magnetic resonance images (MRI) and their stability to variations in time of repetition (TR), time of echo (TE), slice thickness (ST), and pixel spacing (PS) using vegetable phantoms. Methods The organic phantom was realized using two cucumbers placed inside a cylindrical container, and the analysis was performed using T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted images. One dataset was used to test the repeatability of the radiomic features, whereas other four datasets were used to test the sensitivity of the different MRI sequences to image acquisition parameters (TR, TE, ST, and PS). Four regions of interest (ROIs) were segmented: two for the central part of each cucumber and two for the external parts. Radiomic features were extracted from each ROI using Pyradiomics. To assess the effect of preprocessing on the reduction of variability, features were extracted both before and after the preprocessing. The coefficient of variation (CV) and intra-class correlation coefficient (ICC) were used to evaluate variability. Results The use of intensity standardization increased the stability for the first-order statistics features. Shape and size features were always stable for all the analyses. Textural features were particularly sensitive to changes in ST and PS, although some increase in stability could be obtained by voxel size resampling. When images underwent image preprocessing, the number of stable features (ICC > 0.75 and mean absolute CV < 0.3) was 33 for apparent diffusion coefficient (ADC), 52 for T1w, and 73 for T2w. Conclusions The most critical source of variability is related to changes in voxel size (either caused by changes in ST or PS). Preprocessing increases features stability to both test-retest and variation of the image acquisition parameters for all the types of analyzed MRI (T1w, T2w, and ADC), except for ST

Methodology and technology for the development of a prognostic MRI-based radiomic model for the outcome of head and neck cancer patients

The purpose of this study was to establish a methodology and technology for the development of an MRI-based radiomic signature for prognosis of overall survival (OS) in nasopharyngeal cancer from non-endemic areas. The signature was trained using 1072 features extracted from the main tumor in T1-weighted and T2-weighted images of 142 patients. A model with 2 radiomic features was obtained (RAD model). Tumor volume and a signature obtained by training the model on permuted survival data (RADperm model) were used as a reference. A 10-fold cross-validation was used to validate the signature. Harrel's C-index was used as performance metric. A statistical comparison of the RAD, RADperm and volume was performed using Wilcoxon signed rank tests. The C-index for the RAD model was higher compared to the one of the RADperm model (0.69±0.08 vs 0.47±0.05), which ensures absence of overfitting. Also, the signature obtained with the RAD model had an improved C-index compared to tumor volume alone (0.69±0.08 vs 0.65±0.06), suggesting that the radiomic signature provides additional prognostic information

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution

Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients

Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas

Eleven-year management of prostate cancer patients on active surveillance : what have we learned?

PURPOSE: To evaluate the outcomes of active surveillance (AS) on patients with low-risk prostate cancer (PCa) and to identify predictors of disease reclassification. METHODS: In 2005, we defined an institutional AS protocol (Sorveglianza Attiva Istituto Nazionale Tumori [SAINT]), and we joined the Prostate Cancer Research International: Active Surveillance (PRIAS) study in 2007. Eligibility criteria included clinical stage 64T2a, initial prostate-specific antigen (PSA) <10 ng/mL, and Gleason Pattern Score (GPS) 643 + 3 (both protocols); 6425% positive cores with a maximum core length containing cancer 6450% (SAINT); and 642 positive cores and PSA density <0.2 ng/mL/cm3 (PRIAS). Switching to active treatment was advised for a worsening of GPS, increased positive cores, or PSA doubling time <3 years. Active treatment-free survival (ATFS) was assessed using the Kaplan-Meier method. Factors associated with ATFS were evaluated with a multivariate Cox proportional hazards model. RESULTS: A total of 818 patients were included: 200 in SAINT, 530 in PRIAS, and 88 in personalized AS monitoring. Active treatment-free survival was 50% after a median follow-up of 60 months. A total of 404/818 patients (49.4%) discontinued AS: 274 for biopsy-related reclassification, 121/404 (30%) for off-protocol reasons, 9/404 (2.2%) because of anxiety. Biopsy reclassification was associated with PSA density (hazard ratio [HR] 1.8), maximum percentage of core involvement (HR 1.5), positive cores at diagnostic biopsy (HR 1.6), older age (HR 1.5), and prostate volume (HR 0.6) (all p<0.01). Patients from SAINT were significantly more likely to discontinue AS than were the patients from PRIAS (HR 1.65, p<0.0001). CONCLUSIONS: Five years after diagnosis, 50% of patients with early PCa were spared from active treatment. Wide inclusion criteria are associated with lower ATFS. However, at preliminary analysis, this does not seem to affect the probability of unfavorable pathology

The final cut:cell polarity meets cytokinesis at the bud neck in S. cerevisiae

Cell division is a fundamental but complex process that gives rise to two daughter cells. It includes an ordered set of events, altogether called “the cell cycle”, that culminate with cytokinesis, the final stage of mitosis leading to the physical separation of the two daughter cells. Symmetric cell division equally partitions cellular components between the two daughter cells, which are therefore identical to one another and often share the same fate. In many cases, however, cell division is asymmetrical and generates two daughter cells that differ in specific protein inheritance, cell size, or developmental potential. The budding yeast Saccharomyces cerevisiae has proven to be an excellent system to investigate the molecular mechanisms governing asymmetric cell division and cytokinesis. Budding yeast is highly polarized during the cell cycle and divides asymmetrically, producing two cells with distinct sizes and fates. Many components of the machinery establishing cell polarization during budding are relocalized to the division site (i.e., the bud neck) for cytokinesis. In this review we recapitulate how budding yeast cells undergo polarized processes at the bud neck for cell division