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.

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.

We recently demonstrated in a clinical trial the ability of a new protocol, IQ SPECT, to acquire myocardial perfusion imaging (MPI) studies in a quarter of the time (12 s/view) of the standard protocol, with preserved diagnostic accuracy. We now aim to establish the lower limit of radioactivity that can be administered to patients and the minimum acquisition time in SPECT MPI using an IQ SPECT protocol, while preserving diagnostic accuracy. Methods: An anthropomorphic cardiac phantom was used to acquire clinical rest scans with a simulated in vivo distribution of 99m Tc-tetrofosmin at full dose (740 MBq) and at doses equal to 50%, 25%, and 18%. For each dose, 2 sets of images were acquired, with and without a transmural defect (TD). Variable acquisition times were also used for each dose. We analyzed raw data and reconstructed images, including no correction and correction for attenuation (AC), for scatter (SC), or for both (ACSC). Images were evaluated qualitatively and quantitatively in order to assess left ventricle (LV) wall thickness (full width at half maximum of the medial sections), TD, and cavity contrast in the LV wall. Data were compared across different acquisition times within the same dose and across doses with the same acquisition time. Results: Images were visually scored as very-good quality except those acquired with 4 s/view or less at 100% dose and 6 s/view or less with 50%, 25%, or 18% dose, due to falsepositive defects. LV wall thickness was not significantly different among all acquisitions. Cavity contrast remained unchanged within the same dose for all images and tended to be higher in AC and ACSC images. TD contrast remained unchanged within the same dose for all images. In SC and no-correction images, contrast was constant for all doses. AC images had significantly higher TD contrast values, and ACSC images showed a drop in TD contrast for a 50% dose. Conclusion: IQ SPECT effectively preserved both image quality and quantitative measurements with reduced acquisition time or administered dose in a phantom study. These findings suggest that approximately one eighth of the time, compared with standard protocols with a full dose, or a lower dose at an acquisition time of 12 s/view can be applied in MPI without the loss of diagnostic accuracy.

This is the first study investigating how a heart mispositioning can affect diagnostic accuracy with IQ-SPECT system. Mild-to-moderate mispositioning (≤2.5 cm) is unlikely to significantly affect results.

Objectives: To determine interobserver variability in axillary nodal contouring in breast cancer (BC) radiotherapy (RT) by comparing the clinical target volume of participating single centres (SC-CTV) with a gold-standard CTV (GS-CTV). Methods: The GS-CTV of 3 patients (P1, P2, P3) with increasing complexity was created in DICOM format from the median contour of axillary CTVs drawn by BC experts, validated using the simultaneous truth and performance level estimation and peer-reviewed. GS-CTVs were compared with the correspondent SC-CTVs drawn by radiation oncologists, using validated metrics and a total score (TS) integrating all of them. Results: Eighteen RT centres participated in the study. Comparative analyses revealed that, on average, the SC-CTVs were smaller than GS-CTV for P1 and P2 (by −29.25% and −27.83%, respectively) and larger for P3 (by +12.53%). The mean Jaccard index was greater for P1 and P2 compared to P3, but the overlap extent value was around 0.50 or less. Regarding nodal levels, L4 showed the highest concordance with the GS. In the intra patient comparison, L2 and L3 achieved lower TS than L4. Nodal levels showed discrepancy with GS which was not statistically significant for P1, and negligible for P2, while P3 had the worst agreement. DICE Similarity Coefficient did not exceed the minimum threshold for agreement of 0.70 in all the measurements. Conclusions: Substantial differences were observed between SC- and GS-CTV, especially for P3 with altered arm set-up. L2 and L3 were the most critical levels. The study highlighted these key points to address. Advances in knowledge The present study compares, by means of validated geometric indexes, manual segmentationsof axillary lymph nodes in breast cancer from different observers and different institutionsmade on radiotherapy planning computed tomography images. Assessing such variability is ofparamount importance, as geometric uncertainties might lead to incorrect dosimetry andcompromise oncological outcome.