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The complex pathobiology of lung cancer, and its spread worldwide, has prompted research studies that combine radiomic and genomic approaches. Indeed, the early identification of genetic alterations and driver mutations affecting the tumor is fundamental for correctly formulating the prognosis and therapeutic response. In this work, we propose a radiogenomic workflow to detect the presence of KRAS and EGFR mutations using radiomic features extracted from computed tomography images of patients affected by lung adenocarcinoma. To this aim, we investigated several feature selection algorithms to identify the most significant and uncorrelated sets of radiomic features and different classification models to reveal the mutational status. Then, we employed the SHAP (SHapley Additive exPlanations) technique to increase the understanding of the contribution given by specific radiomic features to the identification of the investigated mutations. Two cohorts of patients with lung adenocarcinoma were used for the study. The first one, obtained from the Cancer Imaging Archive (TCIA), consisted of 60 cases (25% EGFR, 23% KRAS); the second one, provided by the Azienda Ospedaliero-Universitaria ’Ospedali Riuniti’ of Foggia, was composed of 55 cases (16% EGFR, 28% KRAS). The best-performing models proposed in our study achieved an AUC of 0.69 and 0.82 on the validation set for predicting the mutational status of EGFR and KRAS, respectively. The Multi-layer Perceptron model emerged as the top-performing model for both oncogenes, in some cases outperforming the state of the art. This study showed that radiomic features can be associated with EGFR and KRAS mutational status in patients with lung adenocarcinoma.

A retrospective study was conducted among Italian cancer healthcare workers (HCWs) to describe how influenza vaccination attitudes have changed during the COVID-19 pandemic. The analysis was conducted on the last three influenza seasons (2018/19, 2019/20 and 2020/21). To account for different relationships and proximity with patients, the study population was grouped into three main professional categories: health personnel, administrative staff and technicians. Moreover, to explore the factors affecting the coverage of influenza vaccine, a multinomial regression analysis was performed.Over the years, the influenza vaccination uptake showed a gradual increase across the overall staff, the highest coverage (53.8%) was observed in the season 2020/21, in particular, for health personnel (57.7%). In general, males resulted in more adherent to vaccination campaigns; nevertheless, this gap decreased in the last season. A total of 28.6% workers were always vaccinated throughout the past three seasons, a remarkable 25.2% (mainly young and females) received for the first time the influenza vaccination in 2020/21.In this dramatic health crisis, the attitudes of HCWs toward flu vaccination have changed. The COVID-19 outbreak increased adherence to flu vaccination, reaching the highest coverage in the campaign 2020/21. However, further efforts should be made to achieve greater vaccination coverage.

The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori “Giovanni Paolo II” and made publicly available to ease research concerning the quantification of tumor cellularity.

Chylous ascites is a rare form of ascites usually associated with cirrhosis, abdominal malignancies, surgeries or infections. We presented a case of chylous ascites after robotic laparoscopic prostatectomy (PLDN-RALP), in which the correct diagnosis was achieved by SPECT/CT lymphoscintigraphy. A 72-year-old male developed chylous ascites after surgery and underwent lymphoscintigraphy with radiolabeled albumin nanocolloids for the supplementary study of the lymph flow and to detect a possible site of leakage. The scintigraphic imaging demonstrated the abdominal effusion and lymph stasis in the left iliac region. The combination of planar imaging with SPECT/CT can resolve the assessment of chylous disorders.

BackgroundAccurate medication reconciliation reduces the risk of drug incompatibilities and adverse events that can occur during transitions in care. Community pharmacies (CPs) are a crucial part of the health care system and could be involved in collecting essential information on conventional and supplementary drugs used at home. ObjectiveThe aim of this paper was to establish an alliance between our cancer institute, Istituto Romagnolo per lo Studio dei Tumori (IRST), and CPs, the latter entrusted with the completion of a pharmacological recognition survey. We also aimed to integrate the national information technology (IT) platform of CPs with the electronic medical records of IRST. MethodsCancer patients undergoing antiblastic treatments were invited to select a CP taking part in the study and to complete the pharmacological recognition step. The information collected by the pharmacist was sent to the electronic medical records of IRST through the new IT platform, after which the oncologist performed the reconciliation process. ResultsA total of 66 CPs completed surveys for 134 patients. An average of 5.9 drugs per patient was used at home, with 12 or more used in the most advanced age groups. Moreover, 60% (80/134) of the patients used nonconventional products or critical foods. Some potential interactions between nonconventional medications and cancer treatments were reported. ConclusionsIn the PROF-1 (Progetto di Rete in Oncologia con le Farmacie di comunità della Romagna) study, an alliance was created between our cancer center and CPs to improve medication reconciliation, and a new integrated IT platform was validated. Trial RegistrationClinicalTrials.gov NCT04796142; https://clinicaltrials.gov/ct2/show/NCT04796142

The inclusive production cross sections at forward rapidity of [Formula: see text], [Formula: see text], [Formula: see text](1S) and [Formula: see text](2S) are measured in [Formula: see text] collisions at [Formula: see text] with the ALICE detector at the LHC. The analysis is based on a data sample corresponding to an integrated luminosity of 1.35 pb[Formula: see text]. Quarkonia are reconstructed in the dimuon-decay channel and the signal yields are evaluated by fitting the [Formula: see text] invariant mass distributions. The differential production cross sections are measured as a function of the transverse momentum [Formula: see text] and rapidity [Formula: see text], over the ranges [Formula: see text] GeV/c for [Formula: see text], [Formula: see text] GeV/c for all other resonances and for [Formula: see text]. The measured cross sections integrated over [Formula: see text] and [Formula: see text], and assuming unpolarized quarkonia, are: [Formula: see text] [Formula: see text]b, [Formula: see text] [Formula: see text]b, [Formula: see text] nb and [Formula: see text] nb, where the first uncertainty is statistical and the second one is systematic. The results are compared to measurements performed by other LHC experiments and to theoretical models.

The inclusive production cross sections at forward rapidity of [Formula: see text], [Formula: see text], [Formula: see text](1S) and [Formula: see text](2S) are measured in [Formula: see text] collisions at [Formula: see text] with the ALICE detector at the LHC. The analysis is based on a data sample corresponding to an integrated luminosity of 1.35 pb[Formula: see text]. Quarkonia are reconstructed in the dimuon-decay channel and the signal yields are evaluated by fitting the [Formula: see text] invariant mass distributions. The differential production cross sections are measured as a function of the transverse momentum [Formula: see text] and rapidity [Formula: see text], over the ranges [Formula: see text] GeV/c for [Formula: see text], [Formula: see text] GeV/c for all other resonances and for [Formula: see text]. The measured cross sections integrated over [Formula: see text] and [Formula: see text], and assuming unpolarized quarkonia, are: [Formula: see text] [Formula: see text]b, [Formula: see text] [Formula: see text]b, [Formula: see text] nb and [Formula: see text] nb, where the first uncertainty is statistical and the second one is systematic. The results are compared to measurements performed by other LHC experiments and to theoretical models.

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

Differential cross sections of charged particles in inelastic pp collisions as a function of pT have been measured at [Formula: see text] at the LHC. The pT spectra are compared to NLO-pQCD calculations. Though the differential cross section for an individual [Formula: see text] cannot be described by NLO-pQCD, the relative increase of cross section with [Formula: see text] is in agreement with NLO-pQCD. Based on these measurements and observations, procedures are discussed to construct pp reference spectra at [Formula: see text] up to pT=50 GeV/c as required for the calculation of the nuclear modification factor in nucleus-nucleus and proton-nucleus collisions.

The ALICE Collaboration at the LHC has measured the J/ψ and ψ' photoproduction at mid-rapidity in ultra-peripheral Pb-Pb collisions at [Formula: see text]. The charmonium is identified via its leptonic decay for events where the hadronic activity is required to be minimal. The analysis is based on an event sample corresponding to an integrated luminosity of about 23 μb-1. The cross section for coherent and incoherent J/ψ production in the rapidity interval -0.9

The ALICE Collaboration at the LHC has measured the J/ψ and ψ' photoproduction at mid-rapidity in ultra-peripheral Pb-Pb collisions at [Formula: see text]. The charmonium is identified via its leptonic decay for events where the hadronic activity is required to be minimal. The analysis is based on an event sample corresponding to an integrated luminosity of about 23 μb-1. The cross section for coherent and incoherent J/ψ production in the rapidity interval -0.9

Differential cross sections of charged particles in inelastic pp collisions as a function of pT have been measured at [Formula: see text] at the LHC. The pT spectra are compared to NLO-pQCD calculations. Though the differential cross section for an individual [Formula: see text] cannot be described by NLO-pQCD, the relative increase of cross section with [Formula: see text] is in agreement with NLO-pQCD. Based on these measurements and observations, procedures are discussed to construct pp reference spectra at [Formula: see text] up to pT=50 GeV/c as required for the calculation of the nuclear modification factor in nucleus-nucleus and proton-nucleus collisions.

Liver segmentation is a crucial step in surgical planning from computed tomography scans. The possibility to obtain a precise delineation of the liver boundaries with the exploitation of automatic techniques can help the radiologists, reducing the annotation time and providing more objective and repeatable results. Subsequent phases typically involve liver vessels’ segmentation and liver segments’ classification. It is especially important to recognize different segments, since each has its own vascularization, and so, hepatic segmentectomies can be performed during surgery, avoiding the unnecessary removal of healthy liver parenchyma. In this work, we focused on the liver segments’ classification task. We exploited a 2.5D Convolutional Neural Network (CNN), namely V-Net, trained with the multi-class focal Dice loss. The idea of focal loss was originally thought as the cross-entropy loss function, aiming at focusing on “hard” samples, avoiding the gradient being overwhelmed by a large number of falsenegatives. In this paper, we introduce two novel focal Dice formulations, one based on the concept of individual voxel’s probability and another related to the Dice formulation for sets. By applying multi-class focal Dice loss to the aforementioned task, we were able to obtain respectable results, with an average Dice coefficient among classes of 82.91%. Moreover, the knowledge of anatomic segments’ configurations allowed the application of a set of rules during the post-processing phase, slightly improving the final segmentation results, obtaining an average Dice coefficient of 83.38%. The average accuracy was close to 99%. The best model turned out to be the one with the focal Dice formulation based on sets. We conducted the Wilcoxon signed-rank test to check if these results were statistically significant, confirming their relevance.

The pseudorapidity density and multiplicity distribution of charged particles produced in proton-proton collisions at the LHC, at a centre-of-mass energy √s= 7 TeV, were measured in the central pseudorapidity region {pipe}η{pipe} < 1. Comparisons are made with previous measurements at √s = 0.9 TeV and 2.36 TeV. At √s = 7 TeV, for events with at least one charged particle in {pipe}η{pipe} < 1, we obtain dNch/dη = 6.01 ± 0.01(stat.)+0.20-0.12(syst.). This corresponds to an increase of 57.6% ± 0.4%(stat.)+3.6-1.8%(syst.) relative to collisions at 0.9 TeV, significantly higher than calculations from commonly used models. The multiplicity distribution at 7 TeV is described fairly well by the negative binomial distribution. © 2010 CERN for the benefit of the ALICE collaboration.

Charged-particle production was studied in proton-proton collisions collected at the LHC with the ALICE detector at centre-of-mass energies 0.9 TeV and 2.36 TeV in the pseudorapidity range {pipe}η{pipe}