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The LCLS-II superconducting electron accelerator at SLAC plans to operate at up to 4 GeV and 240 kW average power, which would create higher radiological impacts particularly near the beam loss points such as beam dumps and halo collimators. The main hazards to the public and environment include direct or skyshine radiation, effluent of radioactive air such as 13N, 15O and 41Ar, and activation of groundwater creating tritium. These hazards were evaluated using analytic methods and FLUKA Monte Carlo code. The controls (mainly extensive bulk shielding and local shielding around high loss points) and monitoring (neutron/photon detectors with detection capabilities below natural background at site boundary, site-wide radioactive air monitors, and groundwater wells) were designed to meet the U.S. DOE and EPA, as well as SLAC requirements. The radiological design and controls for the LCW systems [including concrete housing shielding for 15O and 11C circulating in LCW, 7Be and erosion/corrosion products (22Na, 54Mn, 60Co, 65Zn, etc.) captured in resin and filters, leak detection and containment of LCW with 3H and its waste water discharge; explosion from H2 build-up in surge tank and release of radionuclides] associated with the high power beam dumps are also presented.

Objective: Although artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the available evidence have been inadequate. The aim of this systematic review was to summarize and evaluate the literature on the role of AI applied to ultrasound imaging in benign gynecological disorders., Methods: Web of Science, PubMed and Scopus databases were searched from inception until August 2024. Inclusion criteria were studies applying AI to ultrasound imaging in the diagnosis and management of benign gynecological disorders. Studies retrieved from the literature search were imported into Rayyan software and quality assessment was performed using the Quality Assessment Tool for Artificial Intelligence-Centered Diagnostic Test Accuracy Studies (QUADAS-AI)., Results: Of the 59 studies included, 12 were on polycystic ovary syndrome (PCOS), 11 were on infertility and assisted reproductive technology, 11 were on benign ovarian pathology (i.e. ovarian cysts, ovarian torsion, premature ovarian failure), 10 were on endometrial or myometrial pathology, nine were on pelvic floor disorder and six were on endometriosis. China was the most highly represented country (22/59 (37.3%)). According to QUADAS-AI, most studies were at high risk of bias for the subject selection domain (because the sample size, source or scanner model was not specified, data were not derived from open-source datasets and/or imaging preprocessing was not performed) and the index test domain (AI models were not validated externally), and at low risk of bias for the reference standard domain (the reference standard classified the target condition correctly) and the workflow domain (the time between the index test and the reference standard was reasonable). Most studies (40/59) developed and internally validated AI classification models for distinguishing between normal and pathological cases (i.e. presence vs absence of PCOS, pelvic endometriosis, urinary incontinence, ovarian cyst or ovarian torsion), whereas 19/59 studies aimed to automatically segment or measure ovarian follicles, ovarian volume, endometrial thickness, uterine fibroids or pelvic floor structures., Conclusion: The published literature on AI applied to ultrasound in benign gynecological disorders is focused mainly on creating classification models to distinguish between normal and pathological cases, and on developing models to automatically segment or measure ovarian volume or follicles. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology., (© 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.)

Background: Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of making clinical decisions and providing a more personalized treatment approach for better treatment outcomes. However, the lack of external validation of predictive models, missing in several published articles, is a major issue that can potentially limit the reliability and applicability of predictive models in clinical settings. Therefore, this systematic review described different externally validated methods of predicting response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients and how they could improve clinical decision-making., Method: An extensive search for eligible articles was performed on PubMed, Cochrane, and Scopus between 2018 and 2023, using the keywords: (Response OR outcome) prediction AND (neoadjuvant OR chemoradiotherapy) treatment in 'locally advanced Rectal Cancer'., Inclusion Criteria: (i) Studies including patients diagnosed with LARC (T3/4 and N- or any T and N+) by pre-medical imaging and pathological examination or as stated by the author (ii) Standardized nCRT completed. (iii) Treatment with long or short course radiotherapy. (iv) Studies reporting on the prediction of response to nCRT with pathological complete response (pCR) as the primary outcome. (v) Studies reporting external validation results for response prediction. (vi) Regarding language restrictions, only articles in English were accepted., Exclusion Criteria: (i) We excluded case report studies, conference abstracts, reviews, studies reporting patients with distant metastases at diagnosis. (ii) Studies reporting response prediction with only internally validated approaches., Data Collection and Quality Assessment: Three researchers (DC-D, FB, HT) independently reviewed and screened titles and abstracts of all articles retrieved after de-duplication. Possible disagreements were resolved through discussion among the three researchers. If necessary, three other researchers (LB, GC, MG) were consulted to make the final decision. The extraction of data was performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) template and quality assessment was done using the Prediction model Risk Of Bias Assessment Tool (PROBAST)., Results: A total of 4547 records were identified from the three databases. After excluding 392 duplicate results, 4155 records underwent title and abstract screening. Three thousand and eight hundred articles were excluded after title and abstract screening and 355 articles were retrieved. Out of the 355 retrieved articles, 51 studies were assessed for eligibility. Nineteen reports were then excluded due to lack of reports on external validation, while 4 were excluded due to lack of evaluation of pCR as the primary outcome. Only Twenty-eight articles were eligible and included in this systematic review. In terms of quality assessment, 89 % of the models had low concerns in the participants domain, while 11 % had an unclear rating. 96 % of the models were of low concern in both the predictors and outcome domains. The overall rating showed high applicability potential of the models with 82 % showing low concern, while 18 % were deemed unclear., Conclusion: Most of the external validated techniques showed promising performances and the potential to be applied in clinical settings, which is a crucial step towards evidence-based medicine. However, more studies focused on the external validations of these models in larger cohorts is necessary to ensure that they can reliably predict outcomes in diverse populations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods., (© 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Purpose: Patients diagnosed with High Grade Gliomas (HGG) generally tend to have a relatively negative prognosis with a high risk of early tumor recurrence (TR) after post-operative radio-chemotherapy. The assessment of the pre-operative risk of early versus delayed TR can be crucial to develop a personalized surgical approach. The purpose of this article is to predict TR using MRI radiomic analysis., Methods: Data were retrospectively collected from a database. A total of 248 patients were included based on the availability of 6-month TR results: 188 were used to train the model, the others to externally validate it. After manual segmentation of the tumor, Radiomic features were extracted and different machine learning models were implemented considering a combination of T1 and T2 weighted MR sequences. Receiver Operating Characteristic (ROC) curve was calculated with relative model performance metrics (accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) at the best threshold based on the Youden Index., Results: Models performance were evaluated based on test set results. The best model resulted to be the XGBoost, with an area under ROC curve of 0.72 (95% CI: 0.56 - 0.87). At the best threshold, the model exhibits 0.75 (95% CI: 0.63 - 0.75) as accuracy, 0.62 (95% CI: 0.38 - 0.83) as sensitivity 0.80 (95% CI: 0.66 - 0.89 as specificity, 0.53 (95% CI: 0.31 - 0.73) as PPV, 0.88 (95% CI: 0.72 - 0.94) as NPV., Conclusion: MRI radiomic analysis represents a powerful tool to predict late HGG recurrence, which can be useful to plan personalized surgical treatments and to offer pertinent patient pre-operative counseling., 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 © 2024 Pignotti, Ius, Russo, Bagatto, Beghella Bartoli, Boccia, Boldrini, Chiesa, Ciardi, Cusumano, Giordano, La Rocca, Mazzarella, Mazzucchi, Olivi, Skrap, Tran, Varcasia, Gaudino and Sabatino.)

In multicentric studies, data sharing between institutions might negatively impact patient privacy or data security. An alternative is federated analysis by secure multiparty computation. This pilot study demonstrates an architecture and implementation addressing both technical challenges and legal difficulties in the particularly demanding setting of clinical research on cancer patients within the strict European regulation on patient privacy and data protection: 24 patients from LMU University Hospital in Munich, Germany, and 24 patients from Policlinico Universitario Fondazione Agostino Gemelli, Rome, Italy, were treated for adrenal gland metastasis with typically 40 Gy in 3 or 5 fractions of online-adaptive radiotherapy guided by real-time MR. High local control (21% complete remission, 27% partial remission, 40% stable disease) and low toxicity (73% reporting no toxicity) were observed. Median overall survival was 19 months. Federated analysis was found to improve clinical science through privacy-friendly evaluation of patient data in the European health data space., (© 2024. The Author(s).)

Objective: The primary aim was to identify radiomics ultrasound features that can distinguish between benign and malignant adnexal masses with solid ultrasound morphology, and between primary malignant (including borderline and primary invasive) and metastatic solid ovarian masses, and to develop ultrasound-based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. The secondary aim was to compare the discrimination performance of our newly developed radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and that of subjective assessment by an experienced ultrasound examiner., Methods: This was a retrospective, observational single-center study conducted at Fondazione Policlinico Universitario A. Gemelli IRCC, in Rome, Italy. Included were patients with a histological diagnosis of an adnexal tumor with solid morphology according to International Ovarian Tumor Analysis (IOTA) terminology at preoperative ultrasound examination performed in 2014-2020, who were managed with surgery. The patient cohort was split randomly into training and validation sets at a ratio of 70:30 and with the same proportion of benign and malignant tumors in the two subsets, with malignant tumors including borderline, primary invasive and metastatic tumors. We extracted 68 radiomics features, belonging to two different families: intensity-based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine-tuned using 5-fold cross-validation over the training set, and tested on the held-out validation set. The variables used in model-building were patient age and radiomics features that were statistically significantly different between benign and malignant adnexal masses and assessed as not redundant based on the Pearson correlation coefficient. We evaluated the discriminative ability of the models and compared it to that of the ADNEX model and that of subjective assessment by an experienced ultrasound examiner using the area under the receiver-operating-characteristics curve (AUC) and classification performance by calculating sensitivity and specificity., Results: In total, 326 patients were included and 775 preoperative ultrasound images were analyzed. Of the 68 radiomics features extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 uncorrelated features were selected for inclusion in model-building. The same 52 radiomics features differed significantly between benign, primary malignant and metastatic tumors. However, the values of the features manifested overlapped between primary malignant and metastatic tumors and did not differ significantly between them. In the validation set, 25/98 tumors (25.5%) were benign and 73/98 (74.5%) were malignant (6 borderline, 57 primary invasive, 10 metastatic). In the validation set, a model including only radiomics features had an AUC of 0.80, sensitivity of 0.78 and specificity of 0.76 at an optimal cut-off for risk of malignancy of 68%, based on Youden's index. The corresponding results for a model including age and radiomics features were AUC of 0.79, sensitivity of 0.86 and specificity of 0.56 (cut-off 60%, based on Youden's index), while those of the ADNEX model were AUC of 0.88, sensitivity of 0.99 and specificity of 0.64 (at a 20% risk-of-malignancy cut-off). Subjective assessment had a sensitivity of 0.99 and specificity of 0.72., Conclusions: Our radiomics model had moderate discriminative ability on internal validation and the addition of age to this model did not improve its performance. Even though our radiomics models had discriminative ability inferior to that of the ADNEX model, our results are sufficiently promising to justify continued development of radiomics analysis of ultrasound images of adnexal masses. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology., (© 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.)

Purpose: Magnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance., Materials and Methods: 3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95 th ) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett's S score and Fleiss' kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively., Results: In the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95 th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95 th : p = 0.35) nor for the rectum (DSC: p = 0.32; HD95 th : p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs' boundaries compared with the original MRI., Conclusion: The bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs' auto-segmentation outputs generated by the GAN., 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. The Department of Radiation Oncology of the University Hospital of LMU Munich and the Department of Radiation Oncology of Fondazione Policlinico Universitario “A. Gemelli” IRCCS in Rome have a research agreement with ViewRay. ViewRay had no influence on the study design, the collection or analysis of data, or on the writing of the manuscript. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Vagni, Tran, Catucci, Chiloiro, D’Aviero, Re, Romano, Boldrini, Kawula, Lombardo, Kurz, Landry, Belka, Indovina, Gambacorta, Cusumano and Placidi.)

Purpose: Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow., Methods: 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95 th ) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients., Results: In the internal test set, the 3D and 2D GANs showed DSC/HD95 th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs' volumetric segmentation for a single patient., Conclusions: The proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Background: Predicting clinical outcomes represents a major challenge in Crohn's disease (CD). Radiomics provides a method to extract quantitative features from medical images and may successfully predict clinical course., Aims: The aim of this pilot study is to evaluate the use of radiomics to predict 10-year surgery for CD patients., Methods: We selected a cohort of CD patients with CT scan enterographies and a 10-year follow up. The R library Moddicom was used to extract radiomic features from each lesion of CD, segmented in the CT scans. A logistic regression model based on selected radiomic features was developed to predict 10-year surgery. The model was evaluated by computing the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values (PPV, NPV)., Results: We enroled 30 patients, with 44 CT scans and 93 lesions. We extracted 217 radiomic features from each lesion. The developed model was based on two radiomic features and presented an AUC (95% CI) of 0.83 (0.73-0.91) in predicting 10-year surgery. Sensitivity, specificity, PPV, NPV of the radiomic model were equal to 0.72, 0.90, 0.79, 0.86, respectively., Conclusion: Radiomics could be a helpful tool to identify patients with high risk for surgery and needing a stricter monitoring., Competing Interests: Declaration of Competing Interest All Authors have no conflict of interest to declare, (Copyright © 2022. Published by Elsevier Ltd.)

Background: The aim of this study is to evaluate the delta radiomics approach based on mesorectal radiomic features to develop a model for predicting pathological complete response (pCR) and 2-year disease-free survival (2yDFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (nCRT)., Methods: Pre- and post-nCRT MRIs of LARC patients treated at a single institution from May 2008 to November 2016 were retrospectively collected. Radiomic features were extracted from the GTV and mesorectum. The Wilcoxon-Mann-Whitney test and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the features in predicting pCR and 2yDFS., Results: Out of 203 LARC patients, a total of 565 variables were evaluated. The best performing pCR prediction model was based on two GTV features with an AUC of 0.80 in the training set and 0.69 in the validation set. The best performing 2yDFS prediction model was based on one GTV and two mesorectal features with an AUC of 0.79 in the training set and 0.70 in the validation set., Conclusions: The results of this study suggest a possible role for delta radiomics based on mesorectal features in the prediction of 2yDFS in patients with LARC.

Purpose: Stereotactic body radiotherapy is increasingly used for the treatment of oligometastatic disease. Magnetic resonance-guided stereotactic radiotherapy (MRgSBRT) offers the opportunity to perform dose escalation protocols while reducing the unnecessary irradiation of the surrounding organs at risk. The aim of this retrospective, monoinstitutional study is to evaluate the feasibility and clinical benefit (CB) of MRgSBRT in the setting of oligometastatic patients., Materials and Methods: Data from oligometastatic patients treated with MRgSBRT were collected. The primary objectives were to define the 12-month progression-free survival (PFS) and local progression-free survival (LPFS) and 24-month overall survival (OS) rate. The objective response rate (ORR) included complete response (CR) and partial response (PR). CB was defined as the achievement of ORR and stable disease (SD). Toxicities were also assessed according to the CTCAE version 5.0 scale., Results: From February 2017 to March 2021, 59 consecutive patients with a total of 80 lesions were treated by MRgSBRT on a 0.35 T hybrid unit. CR and PR as well as SD were observed in 30 (37.5%), 7 (8.75%), and 17 (21.25%) lesions, respectively. Furthermore, CB was evaluated at a rate of 67.5% with an ORR of 46.25%. Median follow-up time was 14 months (range: 3-46 months). The 12-month LPFS and PFS rates were 70% and 23%, while 24-month OS rate was 93%. No acute toxicity was reported, whereas late pulmonary fibrosis G1 was observed in 9 patients (15.25%)., Conclusion: MRgSBRT was well tolerated by patients with reported low toxicity levels and a satisfying CB., (© 2023. Italian Society of Medical Radiology.)

To develop a radiological score system to assess the severity of acute cerebellitis (AC) and to compare radiological severity score at the onset to cerebellar atrophy at follow-up. Clinical and MRI findings were recorded in 16 patients with AC. Radiological severity score considering topographic patterns, gray/white matter involvement, enhancement, tonsillar herniation or hydrocephalus development and clinical severity score taking into account clinical symptoms were assessed for each patient at the onset of the symptoms. Radiological and neurological sequelae were assessed at follow-up. At symptoms onset, clinical severity scale ranged from mild to severe and radiological severity score ranged from 3 to 7 with higher scores indicating a greater severity. The cut-off value of 5 for radiological score well segregated severe patients defined by clinical scale. A significant correlation between clinical scale and radiological severity scores (p < 0.001, r = 0.75) was found. At follow-up visit, all children developed cerebellar atrophy and 5 children showed neurologic sequelae while adults showed complete resolution without atrophy. Patients in whom atrophy was not observed had both older ages (p < 0.001) and a focal cerebellar involvement (p = 0.03). In patients with AC, radiological severity score may be a useful tool in evaluating clinical severity, but it is not capable to predict neither neurological sequelae nor evolution towards atrophy. Cerebellar atrophy, observed in children with AC, may be caused by several factors such as the age of patient and the extension of cerebellar involvement and it may be counterbalanced by neuronal restoring processes due to neuroplasticity., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The glioblastoma's bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation., Materials and Methods: We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity ± post-surgical residual mass (CTV&#95;cavity); the CTV a margin of 1.5 cm added to CTV&#95;cavity and the volume resulting from subtracting the CTV&#95;cavity from the CTV was defined as CTV&#95;Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model., Results: Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F&#95;cm&#95;merged.contrast, F&#95;cm&#95;merged.info.corr.2, F&#95;rlm&#95;merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained ( p -value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68-0.88)., Conclusion: This is the first hypothesis-generating study which applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GB patients in order to achieve a margin reduction strategy., 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. The handling editor declared a shared parent affiliation with the authors SC, RR, FB, GS, MC, RG, SL, HT, LB, ND, CV, FP, GD, GD’A, AO, MB, CC, MG, VV, and SG at the time of review., (Copyright © 2023 Chiesa, Russo, Beghella Bartoli, Palumbo, Sabatino, Cannatà, Gigli, Longo, Tran, Boldrini, Dinapoli, Votta, Cusumano, Pignotti, Lupattelli, Camilli, Della Pepa, D’Alessandris, Olivi, Balducci, Colosimo, Gambacorta, Valentini, Aristei and Gaudino.)

We evaluated the accuracy of the quantitative and semiquantitative analysis in detecting regional atrophy patterns and differentiating mild cognitive impairment patients who remain stable (aMCI-S) from patients who develop Alzheimer's disease (aMCI-AD) at clinical follow-up. Baseline magnetic resonance imaging was used for quantitative and semiquantitative analysis using visual rating scales. Visual rating scores were related to gray matter thicknesses or volume measures of some structures belonging to the same brain regions. Receiver operating characteristic (ROC) analysis was performed to assess measures' accuracy in differentiating aMCI-S from aMCI-AD. Comparing aMCI-S and aMCI-AD patients, significant differences were found for specific rating scales, for cortical thickness belonging to the middle temporal lobe (MTL), anterior temporal (AT), and fronto-insular (FI) regions, for gray matter volumes belonging to MTL and AT regions. ROC curve analysis showed that middle temporal atrophy, AT, and FI visual scales showed better diagnostic accuracy than quantitative measures also when thickness measures were combined with hippocampal volumes. Semiquantitative evaluation, performed by trained observers, is a fast and reliable tool in differentiating, at the early stage of disease, aMCI patients that remain stable from those patients that may progress to AD since visual rating scales may be informative both about early hippocampal volume loss and cortical thickness reduction., Competing Interests: Conflict of interest: Cesare Colosimo declares that he is scientific consultant for Bracco Diagnostics Inc. and Bayer HealthCare. The other authors do not state the conflict of interest., (© 2022 Rosalinda Calandrelli et al., published by De Gruyter.)

Purpose: To investigate the potentialities of radiomic analysis and develop radiomic models to predict the skull dysmorphology severity and post-surgical outcome in children with isolated sagittal synostosis (ISS)., Materials and Methods: Preoperative high-resolution CT scans of infants with ISS treated with surgical correction were retrospectively reviewed. The sagittal suture (ROI&#95;entire) and its sections (ROI&#95;anterior/central/posterior) were segmented. Radiomic features extracted from ROI&#95;entire were correlated to the scaphocephalic severity, while radiomic features extracted from ROI&#95;anterior/central/posterior were correlated to the post-surgical outcome. Logistic regression models were built from selected radiomic features and validated to predict the scaphocephalic severity and post-surgical outcome., Results: A total of 105 patients were enrolled in this study. The kurtosis was obtained from the feature selection process for both scaphocephalic severity and post-surgical outcome prediction. The model predicting the scaphocephalic severity had an area under the curve (AUC) of the receiver operating characteristic of 0.71 and a positive predictive value of 0.83 for the testing set. The model built for the post-surgical outcome showed an AUC (95% CI) of 0.75 (0.61;0.88) and a negative predictive value (95% CI) of 0.95 (0.84;0.99)., Conclusion: Our results suggest that radiomics could be useful in quantifying tissue microarchitecture along the mid-suture space and potentially provide relevant biological information about the sutural ossification processes to predict the onset of skull deformities and stratify post-surgical outcome., (© 2022. The Author(s).)

Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions., Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables., Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74)., Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.

Objective: The objective of this study is to find a contrast-enhanced CT-radiomic signature to predict clinical incomplete response in patients affected by hepatocellular carcinoma who underwent locoregional treatments., Patients and Methods: 190 patients affected by hepatocellular carcinoma treated using focal therapies (radiofrequency or microwave ablation) from September 2018 to October 2020 were retrospectively enrolled. Treatment response was evaluated on a per-target-nodule basis on the 6-months follow-up contrast-enhanced CT or MR imaging using the mRECIST criteria. Radiomics analysis was performed using an in-house developed open-source R library. Wilcoxon-Mann-Whitney test was applied for univariate analysis; features with a p-value lower than 0.05 were selected. Pearson correlation was applied to discard highly correlated features (cut-off=0.9). The remaining features were included in a logistic regression model and receiver operating characteristic curves; sensitivity, specificity, positive and negative predictive value were also computed. The model was validated performing 2000 bootstrap resampling., Results: 56 treated lesions from 42 patients were selected. Treatment responses were: complete response for 26 lesions (46.4%), 18 partial responses (32.1%), 10 stable diseases (17.9%), 2 progression diseases (3.6%). Area-Under-Curve value was 0.667 (95% CI: 0.527-0.806); accuracy, sensitivity, specificity, positive and negative predictive values were respectively 0.66, 0.85, 0.50, 0.59 and 0.79., Conclusions: This contrast-enhanced CT-based model can be helpful to early identify poor responder's hepatocellular carcinoma patients and personalize treatments.

The crystal structures of NiX2(pyz)2 (X = Cl (1), Br (2), I (3), and NCS (4)) were determined by synchrotron X-ray powder diffraction. All four compounds consist of two-dimensional (2D) square arrays self-assembled from octahedral NiN4X2 units that are bridged by pyz ligands. The 2D layered motifs displayed by 1-4 are relevant to bifluoride-bridged [Ni(HF2)(pyz)2]EF6 (E = P, Sb), which also possess the same 2D layers. In contrast, terminal X ligands occupy axial positions in 1-4 and cause a staggered packing of adjacent layers. Long-range antiferromagnetic (AFM) order occurs below 1.5 (Cl), 1.9 (Br and NCS), and 2.5 K (I) as determined by heat capacity and muon-spin relaxation. The single-ion anisotropy and g factor of 2, 3, and 4 were measured by electron-spin resonance with no evidence for zero-field splitting (ZFS) being observed. The magnetism of 1-4 spans the spectrum from quasi-two-dimensional (2D) to three-dimensional (3D) antiferromagnetism. Nearly identical results and thermodynamic features were obtained for 2 and 4 as shown by pulsed-field magnetization, magnetic susceptibility, as well as their Néel temperatures. Magnetization curves for 2 and 4 calculated by quantum Monte Carlo simulation also show excellent agreement with the pulsed-field data. Compound 3 is characterized as a 3D AFM with the interlayer interaction (J⊥) being slightly stronger than the intralayer interaction along Ni-pyz-Ni segments (J(pyz)) within the two-dimensional [Ni(pyz)2](2+) square planes. Regardless of X, J(pyz) is similar for the four compounds and is roughly 1 K.

[Ni(HF(2))(3-Clpy)(4)]BF(4) (py = pyridine) is a simple one-dimensional (1D) coordination polymer composed of compressed NiN(4)F(2) octahedra that form chains with bridging HF(2)(-) ligands. In spite of significant distortion of the HF(2)(-) bridge, a quasi-1D antiferromagnetic (AFM) behavior was observed with J(FHF) = 4.86 K.