Evaluation of a synthetic single-crystal diamond detector for relative dosimetry on the Leksell Gamma Knife Perfexion radiosurgery system

Purpose: To evaluate the new commercial PTW-60019 synthetic single-crystal microDiamond detector (PTW, Freiburg, Germany) for relative dosimetry measurements on a clinical Leksell Gamma Knife Perfexion radiosurgery system. Methods: Detector output ratios (DORs) for 4 and 8 mm beams were measured using a micro- Diamond (PTW-60019), a stereotactic unshielded diode [IBA stereotactic field detector (SFD)], a shielded diode (IBA photon field detector), and GafChromic EBT3 films. Both parallel and transversal acquisition directions were considered for PTW-60019 measurements. Measured DORs were compared to the new output factor reference values for Gamma Knife Perfexion (0.814 and 0.900 for 4 and 8 mm, respectively). Profiles in the three directions were also measured for the 4 mm beam to evaluate full width at half maximum (FWHM) and penumbra and to compare them with the corresponding Leksell GammaPlan profiles. Results: FWHM and penumbra for PTW-60019 differed from the calculated values by less than 0.2 and 0.3 mm, for the parallel and transversal acquisitions, respectively. GafChromic films showed FWHM and penumbra within 0.1 mm. The output ratio obtained with the PTW-60019 for the 4 mm field was 1.6% greater in transverse direction compared to the nominal value. Comparable differences up to 0.8% and 1.0% for, respectively, GafChromic films and SFD were found. Conclusions: The microDiamond PTW-60019 is a suitable detector for commissioning and routine use of Gamma Knife with good agreement of both DORs and profiles in the three directions

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Telemedicine in the COVID-19 era: Taking care of children with obesity and diabetes mellitus

Severe acute respiratory syndrome coronavirus 2 infection was declared a pandemic in January 2020. Since then, several measures to limit virus transmission have been imposed; among them, home confinement has been the most severe, with drastic changes in the daily routines of the general population. The "stay at home" rule has impaired healthcare service access, and patients with chronic conditions were the most exposed to the negative effects of this limitation. There is strong evidence of the worsening of obesity and diabetes mellitus in children during this period. To overcome these issues, healthcare providers have changed their clinical practice to ensure follow-up visits and medical consultation though the use of telemedicine. Telemedicine, including telephone calls, videocalls, data platforms of shared telemedicine data platforms mitigated the negative effect of pandemic restrictions. Published evidence has documented good metabolic control and weight management outcomes in centers that performed extensive telemedicine services last year during the pandemic. This review discusses studies that investigated the use of telemedicine tools for the management of pediatric obesity and diabetes

Does the Aortic Annulus Dilate After Aortic Root Remodeling?

Background The 2 main techniques of valve-sparing aortic root replacement (VSRR) are remodeling and reimplantation. There is concern that the aortic annulus, which is not stabilized in remodeling technique, may dilate over time and cause aortic regurgitation. Our aim was to assess whether the aortic annulus dilates after VSRR with remodeling technique without aortic annuloplasty. Methods Data on patients undergoing elective or urgent VSRR remodeling technique between 2005 and 2018 were collected. Patients undergoing arch and emergency surgery for acute type A aortic dissection were excluded. Preoperative aortic annulus diameter was measured by transthoracic echocardiography, and this was compared with the annulus diameter measured from the most recently available transthoracic echocardiography. The requirement for reintervention during follow-up was recorded. Results Between 2005 and 2018, 98 patients underwent VSRR. Sixty-six (67.3%) had Marfan syndrome or Loeys-Dietz syndrome. Median age was 60 (interquartile range, 18-68) years and 71 (72.4%) were men. Median cross-clamp and cardiopulmonary bypass times were 122 (interquartile range, 104-164) minutes and 138 (interquartile range, 121-198) minutes, respectively. Median intensive care unit and hospital stay were 1 day and 6 days, respectively. No patients suffered perioperative stroke. There was no in-hospital mortality. At median follow-up of 7.1 years (interquartile range, 5-129 months), mean postoperative annular diameter was 25.7 mm, from 24.2 mm preoperatively (P = .403). One patient required aortic valve replacement during follow-up. Freedom from moderate or severe aortic regurgitation was 97%. Conclusions There was no significant aortic annular dilatation in selected patients undergoing remodeling VSRR. Our data do not support routine use of annuloplasty in patients with annular diameter less than or equal to 25 mm

Magnetic susceptibility as a 1-year predictor of outcome in familial cerebral cavernous malformations: a pilot study

Objectives: To test whether quantitative susceptibility mapping (QSM) of cerebral cavernous malformations (CCMs) assessed at baseline may predict the presence or absence of haemorrhagic signs at 1-year follow-up. Methods: Familial CCM patients were enrolled in the longitudinal multicentre study Treat-CCM. The 3-T MRI scan allowed performing a semi-automatic segmentation of CCMs and computing the maximum susceptibility in each segmented CCM (QSMmax) at baseline. CCMs were classified as haemorrhagic and non-haemorrhagic at baseline and then subclassified according to the 1-year (t1) evolution. Between-group differences were tested, and the diagnostic accuracy of QSMmax in predicting the presence or absence of haemorrhagic signs in CCMs was calculated with ROC analyses. Results: Thirty-three patients were included in the analysis, and a total of 1126 CCMs were segmented. QSMmax was higher in haemorrhagic CCMs than in non-haemorrhagic CCMs (p < 0.001). In haemorrhagic CCMs at baseline, the accuracy of QSMmax in differentiating CCMs that were still haemorrhagic from CCMs that recovered from haemorrhage at t1 calculated as area under the curve (AUC) was 0.78 with sensitivity 62.69%, specificity 82.35%, positive predictive value (PPV) 93.3% and negative predictive value (NPV) 35.9% (QSMmax cut-off ≥ 1462.95 ppb). In non-haemorrhagic CCMs at baseline, AUC was 0.91 in differentiating CCMs that bled at t1 from stable CCMs with sensitivity 100%, specificity 81.9%, PPV 5.1%, and NPV 100% (QSMmax cut-off ≥ 776.29 ppb). Conclusions: The QSMmax in CCMs at baseline showed high accuracy in predicting the presence or absence of haemorrhagic signs at 1-year follow-up. Further effort is required to test the role of QSM in follow-up assessment and therapeutic trials in multicentre CCM studies. Key points: • QSM in semi-automatically segmented CCM was feasible. • The maximum magnetic susceptibility in a single CCM at baseline may predict the presence or absence of haemorrhagic signs at 1-year follow-up. • Multicentric studies are needed to enforce the role of QSM in predicting the CCMs' haemorrhagic evolution in patients affected by familial and sporadic forms