Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features.

BACKGROUND: Possible advantages of magnetic resonance (MR)-guided radiation therapy (MRgRT) for the treatment of brain tumors include improved definition of treatment volumes and organs at risk (OARs) that could allow margin reductions, resulting in limited dose to the OARs and/or dose escalation to target volumes. Recently, hybrid systems integrating a linear accelerator and an magnetic resonance imaging (MRI) scan (MRI-linacs, MRL) have been introduced, that could potentially lead to a fully MRI-based treatment workflow. METHODS: We performed a systematic review of the published literature regarding the adoption of MRL for the treatment of primary or secondary brain tumors (last update November 3, 2022), retrieving a total of 2487 records; after a selection based on title and abstracts, the full text of 74 articles was analyzed, finally resulting in the 52 papers included in this review. RESULTS AND DISCUSSION: Several solutions have been implemented to achieve a paradigm shift from CT-based radiotherapy to MRgRT, such as the management of geometric integrity and the definition of synthetic CT models that estimate electron density. Multiple sequences have been optimized to acquire images with adequate quality with on-board MR scanner in limited times. Various sophisticated algorithms have been developed to compensate the impact of magnetic field on dose distribution and calculate daily adaptive plans in a few minutes with satisfactory dosimetric parameters for the treatment of primary brain tumors and cerebral metastases. Dosimetric studies and preliminary clinical experiences demonstrated the feasibility of treating brain lesions with MRL. CONCLUSIONS: The adoption of an MRI-only workflow is feasible and could offer several advantages for the treatment of brain tumors, including superior image quality for lesions and OARs and the possibility to adapt the treatment plan on the basis of daily MRI. The growing body of clinical data will clarify the potential benefit in terms of toxicity and response to treatment.

A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data.

COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.

SARS-CoV-2-related encephalitis with prominent parkinsonism: clinical and FDG-PET correlates in two patients.

Considering the similarities with other pandemics due to respiratory virus infections and subsequent development of neurological disorders (e.g. encephalitis lethargica after the 1918 influenza), there is growing concern about a possible new wave of neurological complications following the worldwide spread of SARS-CoV-2. However, data on COVID-19-related encephalitis and movement disorders are still limited. Herein, we describe the clinical and neuroimaging (FDG-PET/CT, MRI and DaT-SPECT) findings of two patients with COVID-19-related encephalopathy who developed prominent parkinsonism. None of the patients had previous history of parkinsonian signs/symptoms, and none had prodromal features of Parkinson's disease (hyposmia or RBD). Both developed a rapidly progressive form of atypical parkinsonism along with distinctive features suggestive of encephalitis. A possible immune-mediated etiology was suggested in Patient 2 by the presence of CSF-restricted oligoclonal bands, but none of the patients responded favorably to immunotherapy. Interestingly, FDG-PET/CT findings were similar in both cases and reminiscent of those observed in post-encephalitic parkinsonism, with cortical hypo-metabolism associated with hyper-metabolism in the brainstem, mesial temporal lobes, and basal ganglia. Patient's FDG-PET/CT findings were validated by performing a Statistical Parametric Mapping analysis and comparing the results with a cohort of healthy controls (n = 48). Cerebrum cortical thickness map was obtained in Patient 1 from MRI examinations to evaluate the structural correlates of the metabolic alterations detected with FDG-PET/CT. Hypermetabolic areas correlated with brain regions showing increased cortical thickness, suggesting their involvement during the inflammatory process. Overall, these observations suggest that SARS-CoV-2 infection may trigger an encephalitis with prominent parkinsonism and distinctive brain metabolic alterations.

MRI only in a patient with prostate cancer with bilateral metal hip prostheses: case study.

OBJECTIVE: To outline a practical method of performing prostate cancer radiotherapy in patients with bilateral metal hip prostheses with the standard resources available in a modern general hospital. The proposed workflow is based exclusively on magnetic resonance imaging (MRI) to avoid computed tomography (CT) artifacts. CASE DESCRIPTION: This study concerns a 73-year-old man with bilateral hip prostheses with an elevated risk prostate cancer. Magnetic resonance images with assigned electron densities were used for planning purposes, generating a synthetic CT (sCT). Imaging acquisition was performed with an optimized Dixon sequence on a 1.5T MRI scanner. The images were contoured by autosegmentation software, based on an MRI database of 20 patients. The sCT was generated assigning averaged electron densities to each contour. Two volumetric modulated arc therapy plans, a complete arc and a partial one, where the beam entrances through the prostheses were avoided for about 50° on both sides, were compared. The feasibility of matching daily cone beam CT (CBCT) with MRI reference images was also tested by visual evaluations of different radiation oncologists. CONCLUSIONS: The use of magnetic resonance images improved accuracy in targets and organs at risk (OARs) contouring. The complete arc plan was chosen because of 10% lower mean and maximum doses to prostheses with the same planning target volume coverage and OAR sparing. The image quality of the match between performed CBCTs and MRI was considered acceptable. The proposed method seems promising to improve radiotherapy treatments for this complex category of patients.

A single phantom, a single statistical method for low-contrast detectability assessment.

PURPOSE: The assessment of low-contrast-details is a part of the quality control (QC) program in digital radiology. It generally consists of evaluating the threshold contrast (Cth) detectability details for different-sized inserts, appropriately located in dedicated QC test tools. This work aims to propose a simplified method, based on a statistical model approach for threshold contrast estimation, suitable for different modalities in digital radiology. METHODS: A home-madelow-contrast phantom, made of a central aluminium insert with a step-wedge, was assembled and tested. The reliability and robustness of the method were investigated for Mammography, Digital Radiography, Fluoroscopy and Angiography. Imageswere analysed using our dedicated software developed on Matlab®. TheCth is expressed in the same unit (mmAl) for all studied modalities. RESULTS: This method allows the collection of Cthinformation from different modalities and equipment by different vendors, and it could be used to define typical values. Results are summarized in detail. For 0.5 diameter detail, Cthresults are in the range of: 0.018-0.023 mmAl for 2D mammography and 0.26-0.34 mmAl DR images. For angiographic images, for 2.5 mm diameter detail, the Cths median values are 0.55, 0.4, 0.06, 0.12 mmAl for low dose fluoroscopy, coronary fluorography, cerebral and abdominal DSA, respectively. CONCLUSIONS: The statistical method proposed in this study gives a simple approach for Low-Contrast-Details assessment, and the typical values proposed can be implemented in a QA program for digital radiology modalities.