Alessia Giuliano

Background/Objectives: Glioblastoma (GBM) is the most malignant subtype of glioma and shows the poorest prognosis with a median survival time of 15 months. The methylation status of the Methylguanine-DNA Methyltransferase (MGMT) was proven to be a crucial factor in selecting the most appropriate therapy. Currently, it is assessed through brain biopsy, which is a highly invasive and very expensive technique. For these reasons, in recent years, the possibility of inferring this information from multi-parametric Magnetic Resonance Imaging (mpMRI) has been widely explored. However, substantial differences in performance are reported in the literature. Methods: In this study, we developed several models based on either radiomic or deep learning approaches and a mixture of them using mpMRI for the MGMT status assessment using the public dataset UPENN-GBM, available on The Cancer Imaging Archive. Despite the tests performed using all MRI acquisitions and different methodological approaches, we did not obtain sufficiently reliable performance to direct the therapeutic path of patients. We thus investigated the impact of segmentation quality on MGMT status prediction since the UPENN-GBM dataset contains both automatic and manual refined segmentation masks. Results: We found that performance obtained through radiomic features computed on manually segmented tumors was significantly higher compared to that obtained using automatic segmentation, even when the differences between segmentation masks, measured in terms of Dice Similarity Coefficient (DSC), is not significantly different. Conclusion: This could be the reason why very different MGMT classification performance is typically reported and suggests the creation of a benchmark dataset, with high-quality segmentation masks.

Quantitative analyses in nuclear medicine are increasingly used, both for diagnostic and therapeutic purposes. The Partial Volume Effect (PVE) is the most important factor of loss of quantification in Nuclear Medicine, especially for evaluation in Region of Interest (ROI) smaller than the Full Width at Half Maximum (FWHM) of the PSF. The aim of this work is to present a new approach for the correction of PVE, using a post-reconstruction process starting from a mathematical expression, which only requires the knowledge of the FWHM of the final PSF of the imaging system used. After the presentation of the theoretical derivation, the experimental evaluation of this method is performed using a PET/CT hybrid system and acquiring the IEC NEMA phantom with six spherical “hot” ROIs (with diameters of 10, 13, 17, 22, 28, and 37 mm) and a homogeneous “colder” background. In order to evaluate the recovery of quantitative data, the effect of statistical noise (different acquisition times), tomographic reconstruction algorithm with and without time-of-flight (TOF) and different signal-to-background activity concentration ratio (3:1 and 10:1) was studied. The application of the corrective method allows recovering the loss of quantification due to PVE for all sizes of spheres acquired, with a final accuracy less than 17%, for lesion dimensions larger than two FWHM and for acquisition times equal to or greater than two minutes.

The identification of reliable brain endophenotypes of autism spectrum disorders (ASD) has been hampered to date by the heterogeneity in the neuroanatomical abnormalities detected in this condition. To handle the complexity of neuroimaging data and to convert brain images in informative biomarkers of pathology, multivariate analysis techniques based on Support Vector Machines (SVM) have been widely used in several disease conditions. They are usually trained to distinguish patients from healthy control subjects by making a binary classification. Here, we propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast to two-class classification, is based on a description of one class of objects only. This approach, by defining a multivariate normative rule on one class of subjects, allows recognizing examples from a different category as outliers. We applied the OCC to 314 regional features extracted from brain structural Magnetic Resonance Imaging (MRI) scans of young children with ASD (21 males and 20 females) and control subjects (20 males and 20 females), matched on age [range: 22–72 months of age; mean=49 months] and non-verbal intelligence quotient (NVIQ) [range: 31–123; mean=73]. We demonstrated that a common pattern of features characterize the ASD population. The OCC SVM trained on the group of ASD subjects showed the following performances in the ASD vs. controls separation: the area under the receiver operating characteristic curve (AUC) was 0.74 for the male and 0.68 for the female population, respectively. Notably, the ASD vs. controls discrimination results were maximized when evaluated on the subsamples of subjects with NVIQ ≥ 70, leading to AUC= 0.81 for the male and AUC = 0.72 for the female populations, respectively. Language regions and regions from the default mode network –posterior cingulate cortex, pars opercularis and pars triangularis of the inferior frontal gyrus, and transverse temporal gyrus– contributed most to distinguishing individuals with ASD from controls, arguing for the crucial role of these areas in the ASD pathophysiology. The observed brain patterns associate preschoolers with ASD independently of their age, gender and NVIQ and therefore they are expected to constitute part of the ASD brain endophenotype.