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Nuclear fragmentation studies, historically driven by the interest of the nuclear physicists, are nowadays the subject of great attention for the hadrontherapy and the space radiation protection communities. In both fields, fragmentation cross sections are needed to predict the effects of the ion nuclear interactions within the patient’s and the astronaut’s body. Indeed, the the Monte Carlo codes used in planning tumor treatments and space missions must be tuned and validated by experimental data. However, only a limited set of fragmentation cross sections are available in literature, especially at Fermi energies. Therefore we have studied the production of secondary fragments in the 12C+12C and 12C+ 197Au reactions at 62 AMeV. Some of the measured cross sections are presented in this work.

Over the last twenty years, there has been a renewed interest in nuclear fragmentation studies for both hadrontherapy applications and space radiation protection. In both fields, fragmentation cross sections are needed to predict the effects of the ions nuclear interactions within the patient's and the astronaut's body. Indeed, the Monte Carlo codes used in planning tumor treatments and space missions must be tuned and validated by experimental data. However, only a limited set of fragmentation cross sections are available in literature, especially at Fermi energies. Therefore, we have studied the production of secondary fragments in the 12C+ 12C and 12C+ 197Au reactions at 62 AMeV. In this work, the measured 4He cross sections angular distributions at four selected angles are presented and compared. [ABSTRACT FROM AUTHOR]

Nuclear fragmentation measurements are necessary when using heavy-ion beams in hadrontherapy to predict the effects of the ion nuclear interactions within the human body. Moreover, they are also fundamental to validate and improve the Monte Carlo codes for their use in planning tumor treatments. Nowadays, a very limited set of carbon fragmentation cross sections are being measured, and in particular, to our knowledge, no double-differential fragmentation cross sections at intermediate energies are available in the literature. In this work, we have measured the double-differential cross sections and the angular distributions of the secondary fragments produced in the 12C fragmentation at 62 A MeV on a thin carbon target. The experimental data have been used to benchmark the prediction capability of the GEANT4 Monte Carlo code at intermediate energies, where it was never tested before. In particular, we have compared the experimental data with the predictions of two GEANT4 nuclear reaction models: the Binary Light Ions Cascade and the Quantum Molecular Dynamic. From the comparison, it has been observed that the Binary Light Ions Cascade approximates the angular distributions of the fragment production cross sections better than the Quantum Molecular Dynamic model. However, the discrepancies observed between the experimental data and the Monte Carlo simulations lead to the conclusion that the prediction capability of both models needs to be improved at intermediate energies [ABSTRACT FROM AUTHOR]

Background: Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18 F-Cho PET/CT imaging features to predict disease progression in PCa., Methods: We retrospectively analyzed high-risk PCa patients who underwent restaging 18 F-Cho PET/CT from November 2013 to May 2018. 18 F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model has been adapted using NCA for feature selection, while DA was used as a method for feature classification and performance analysis., Results: One hundred and six imaging features were extracted for 46 lesions for a total of 4876 features analyzed. No significant differences between the training and validating sets in terms of age, sex, PSA values, lesion location and size (P>0.05) were demonstrated by the machine-learning model. Thirteen features were able to discriminate FU disease status after NCA selection. Best performance in DA classification was obtained using the combination of the 13 selected features (sensitivity 74%, specificity 58% and accuracy 66%) compared to the use of all features (sensitivity 40%, specificity 52%, and accuracy 51%). Per-site performance of the 13 selected features in DA classification were as follows: T = sensitivity 63%, specificity 83%, accuracy 71%; N = sensitivity 87%, specificity 91% of and accuracy 90%; bone-M = sensitivity 33%, specificity 77% and accuracy 66%., Conclusions: An artificial intelligence model demonstrated to be feasible and able to select a panel of 18 F-Cho PET/CT features with valuable association with PCa patients' outcome.

Albeit the pathogenesis of COVID-19 remains unclear, host's genetic polymorphisms in genes involved in infection and reinfection, inflammation, or immune stimulation could play a role in determining the course and outcome. We studied in the early phase of pandemic consecutive patients ( N = 383) with SARS-CoV-2 infection, whose subsequent clinical course was classified as mild or severe, the latter being characterized by admission to intensive therapy unit or death. Five host gene polymorphisms (MERTK rs4374383, PNPLA3 rs738409, TLL-1 rs17047200, IFNL3 rs1297860, and INFL4 rs368234815) were assessed by using whole nucleic acids extracted from nasopharyngeal swabs. Specific protease cleavage sites of TLL-1 on the SARS-CoV-2 Spike protein were predicted in silico . Male subjects and older patients were significantly at higher risk for a severe outcome ( p = 0.02 and p < 0.001, respectively). By considering patients ≤65 years, after adjusting for potential confounding due to sex, an increased risk of severe outcome was found in subjects with the GG genotype of PNPLA3 (adj-OR: 4.69; 95% CI = 1.01-22.04) or TT genotype of TLL-1 (adj-OR=9.1; 95% CI = 1.45-57.3). In silico evaluation showed that TLL-1 is potentially involved in the Spike protein cleavage which is essential for viral binding and entry into the host cells using the host receptor angiotensin-converting enzyme 2 (ACE2). Subjects carrying a GG genotype in PNPLA3 gene might have a constitutive upregulation of the NLRP3 inflammasome and be more prone to tissue damage when infected by SARS-CoV-2. The TT genotype in TLL-1 gene might affect its protease activity on the SARS-CoV-2 Spike protein, enhancing the ability to infect or re-infect host's cells. The untoward effect of these variants on disease course is evident in younger patients due to the relative absence of comorbidities as determinants of prognosis. In the unresolved pathogenetic scenery of COVID-19, the identification of genetic variants associates with more prolonged course or with a severe outcome of infection would support the development of predictive tools useful to stratify subjects by risk class at presentation. Moreover, the individuation of key genes could contribute to a better understanding of the pathways involved in the pathogenesis, giving the basis for rational therapeutic approaches., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Grimaudo, Amodio, Pipitone, Maida, Pizzo, Prestileo, Tramuto, Sardina, Vitale, Casuccio and Craxì.)

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician's requirements in a radiotherapy environment.

Background: In this paper the clinical value of PET for early prediction of tumor response to erlotinib in patients with advanced or metastatic non-small cell lung cancer (NSCLC) after failure of at least one prior chemotherapy regimen is evaluated. The aim was to compare the early metabolic treatment response using European Organization for Research and Treatment of Cancer (EORTC) 1999 recommendations and PET Response Criteria in Solid Tumors (PERCIST), and the standard treatment response using Response Evaluation Criteria in Solid Tumors (RECIST)., Methods: Twenty patients with stage IV NSCLC were enrolled prospectively. PET/CT studies were performed before, then 48 hours, and 45 days after the initiation of erlotinib treatment. The lesion with the highest uptake in each patient was evaluated according to EORTC 1999 recommendations, PERCIST and RECIST to assess metabolic and anatomic response. Response classifications were compared statistically using Wilcoxon signed-rank test. Disease-free survival (DFS) and overall survival (OS) were calculated by the Kaplan-Meier Test., Results: At 48 hours, the Kaplan-Meier analysis showed that EORTC proved to be a significant prognostic factor for predicting DFS and OS. At 45 days, there was a significant difference in response evaluation between RECIST and metabolic classifications. RECIST and PERCIST were significant prognostic factors for predicting DFS and OS. EORTC was not able to discriminate responder from non-responder patients., Conclusions: This study shows that, according to the EORTC protocol, the PET exam is able to provide early identification of patients who benefit from Erlotinib treatment. Used at the end of therapy, PERCIST could be considered an appropriate metabolic evaluation method to discriminate responders from non-responders.