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Positron emission tomography/magnetic resonance imaging (PET/MRI) is an innovative imaging technology that allows the simultaneous acquisition of metabolic, structural, and functional information for an accurate characterization of tissues. The qualitative interpretation of PET/MR images has shown great potential in tumor diagnosis, treatment planning, and follow-up. Furthermore, PET/MRI also potentially provides several quantitative imaging biomarkers, also called “radiomic features,” for tumor characterization extracted both from PET and MRI. In the new era of precision medicine, radiomics is an emerging translational field of research aiming to extract a large number of quantitative features followed by their interpretation through various analyses and integration into predictive models. In this chapter, different clinical applications of radiomics based on PET, MRI, and PET/MR images will be presented, with a specific focus on the characterization of brain tumors, breast cancer, pancreatic neuroendocrine tumors, and prostate cancer. Finally, some other applications of artificial intelligence applied to PET/MR images that can shortly enter into clinical practice will be shown.

We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: “(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)” have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.

The increasing use of mechanical thrombectomy in stroke management has opened the window to local intraarterial brain delivery of therapeutic agents. In this context, the use of nanomedicine could further improve the delivery of new treatments for specific brain targeting, tracking and guidance. In this study we take advantage of this new endovascular approach to deliver biocompatible poly(D-L-lactic-co-glycolic acid) (PLGA) nanocapsules functionalized with superparamagnetic iron oxide nanoparticles and Cy7.5 for magnetic targeting, magnetic resonance and fluorescent molecular imaging. A complete biodistribution study in naïve (n = 59) and ischemic (n = 51) mice receiving intravenous or intraarterial nanocapsules, with two different magnet devices and imaged from 30 min to 48 h, showed an extraordinary advantage of the intraarterial route for brain delivery with a specific improvement in cortical targeting when using a magnetic device in both control and ischemic conditions. Safety was evaluated in ischemic mice (n = 69) showing no signs of systemic toxicity nor increasing mortality, infarct lesions or hemorrhages. In conclusion, the challenging brain delivery of therapeutic nanomaterials could be efficiently and safely overcome with a controlled endovascular administration and magnetic targeting, which could be considered in the context of endovascular interventions for the delivery of multiple treatments for stroke.

Purpose: To explore the role of fully hybrid 68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine tumours (PanNETs) undergoing surgery. Methods: One hundred eighty-seven consecutive 68Ga-DOTATOC PET/MRI scans (March 2018–June 2020) performed for gastroenteropancreatic neuroendocrine tumour were retrospectively evaluated; 16/187 patients met the eligibility criteria (68Ga-DOTATOC PET/MRI for preoperative staging of PanNET and availability of histological data). PET/MR scans were qualitatively and quantitatively interpreted, and the following imaging parameters were derived: PET-derived SUVmax, SUVmean, somatostatin receptor density (SRD), total lesion somatostatin receptor density (TLSRD), and MRI-derived apparent diffusion coefficient (ADC), arterial and late enhancement, necrosis, cystic degeneration, and maximum diameter. Additionally, first-, second-, and higher-order radiomic parameters were extracted from both PET and MRI scans. Correlations with several PanNETs’ histopathological prognostic factors were evaluated using Spearman’s coefficient, while the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate parameters’ predictive performance. Results: Primary tumour was detected in all 16 patients (15/16 by 68Ga-DOTATOC PET and 16/16 by MRI). SUVmax and SUVmean resulted good predictors of lymphnodal (LN) involvement (AUC of 0.850 and 0.783, respectively). Second-order radiomic parameters GrayLevelVariance and HighGrayLevelZoneEmphasis extracted from T2 MRI demonstrated significant correlations with LN involvement (adjusted p = 0.009), also showing good predictive performance (AUC = 0.992). Conclusion: This study demonstrates the role of the fully hybrid PET/MRI tool for the synergic function of imaging parameters extracted by the two modalities and highlights the potentiality of imaging and radiomic parameters in assessing h

Aim: Unspecific bone uptake is one of the main limitations of PET imaging with some PSMA-targeting radiopharmaceuticals, especially with [18F]PSMA-1007. We explored the potential association between osteoporosis and the occurrence of unspecific [18F]PSMA-1007 bone uptake investigating markers which might correlate with bone mineral density. Materials and methods: We retrospectively analyzed treatment-naïve patients with a confirmed diagnosis of prostate adenocarcinoma who underwent staging [18F]PSMA-1007 positron emission tomography (PET). Qualitative image analysis was performed independently by three experienced nuclear medicine physicians. Patients were divided in two groups according to the presence/absence of unspecific bone uptake. Clinical information, blood count parameters (assessed within 3 months to the PET scan), body mass index (BMI), and bone density as estimated by computed tomography were collected. The Kruskal–Wallis and t-test were used to compare parameters. Results: We analyzed 77 patients: 29 of them (38%) had unspecific bone uptake at [18F]PSMA-1007 PET, most commonly in the pelvic bones (69%) and ribs (62%). We did not find any significant difference in clinical parameters in the two groups. In patients with unspecific bone uptake, white blood cell, and neutrophil counts were significantly higher; in the same group, we observed lower values of BMI and bone density, although not statistically different. Conclusions: We observed unspecific bone uptake on [18F]PSMA-1007 PET in more than 1/3 of patients. In this exploratory analysis, we found a significant correlation between blood count parameters and unspecific [18F]PSMA-1007 bone uptake. We may speculate that [18F]PSMA-1007 unspecific bone uptake could be associated with osteoporosis. This hypothesis needs to be further investigated in larger populations and exploring more specific markers of osteoporosis.

Localized prostate cancer (PCa) can be treated with radical prostatectomy (RP). Up to 30% of patients undergoing this procedure experience biochemical recurrence (BCR), namely the rise in serum prostate-specific antigen (PSA) levels during the post-surgical follow-up, requiring further treatments and with the risk of severe disease progression. Currently, the most accurate imaging technique to confirm, detect, and locate disease relapses in BCR patients is prostate-specific membrane antigen (PSMA)-targeted PET, as recommended by international clinical guidelines. The aim of the study was to investigate potential clinical and pathological predictors of PSMA PET positivity, validated by clinical and instrumental follow-up or histopathological data. In this study, a selected cohort of BCR patients after RP and no other PCa-related therapy who underwent either PSMA PET/CT or PSMA PET/MRI has been analysed. Among the considered predictors, both pathological staging after RP equal or higher than pT3a and higher PSA levels at the time of the scan were significantly correlated with PSMA PET positivity on multivariate logistic regression analysis. As expected, PSMA PET confirmed its role as an accurate imaging technique in the setting of BCR in PCa. These findings may inform appropriate and tailored patient selection and scan timing to optimize and fully exploit this powerful diagnostic tool.

The aim of the present study is to investigate the synergic role of68Ga-PSMA PET/MRI and68Ga-DOTA-RM2 PET/MRI in prostate cancer (PCa) staging. We present pilot data on twenty-two patients with biopsy-proven PCa that underwent68Ga-PSMA PET/MRI for staging purposes, with 19/22 also undergoing68Gaa-DOTA-RM2 PET/MRI. TNM classification based on image findings was performed and quantitative imaging parameters were collected for each scan. Furthermore, twelve patients underwent radical prostatectomy with the availability of histological data that were used as the gold standard to validate intraprostatic findings. A DICE score between regions of interest manually segmented on the primary tumour on68Ga-PSMA PET,68Ga-DOTA-RM2 PET and on T2 MRI was computed. All imaging modalities detected the primary PCa in 18/19 patients, with68Ga-DOTA-RM2 PET not detecting any lesion in 1/19 patients. In the remaining patients,68Ga-PSMA and MRI were concordant. Seven patients presented seminal vesicles involvement on MRI, with two of these being also detected by68Ga-PSMA, and68Ga-DOTA-RM2 PET being negative. Regarding extraprostatic disease,68Ga-PSMA PET,68Ga-DOTA-RM2 PET and MRI resulted positive in seven, four and five patients at lymph-nodal level, respectively, and at a bone level in three, zero and one patients, respectively. These preliminary results suggest the potential complementary role of68Ga-PSMA PET,68Ga-DOTA-RM2 PET and MRI in PCa characterization during the staging phase.

Purpose: To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided. Methods: Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction. Results: Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response. Conclusions: Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.

Aim To explore the potentiality of radiomics analysis, performed on 68Ga-DOTATOC and fluorine-18-fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT) images, in predicting tumour aggressiveness and outcome in patients candidate to surgery for pancreatic neuroendocrine neoplasms (PanNENs). Patients and methods Retrospective study including 61 patients who underwent 68Ga-DOTATOC and 18F-FDG PET/CT before surgery for PanNEN. Semiquantitative variables [SUVmax and somatostatin receptor density (SRD) for 68Ga-DOTATOC PET; SUVmax and MTV for 18F-FDG PET] and texture features [intensity variability, size zone variability (SZV), zone percentage, entropy; homogeneity, dissimilarity and coefficient of variation (Co-V)] have been analysed to evaluate their possible role in predicting tumour characteristics. Principal component analysis (PCA) was firstly performed and then multiple regression analyses were performed by using the extracted principal components. Results Regarding 68Ga-DOTATOC PET, SZV, entropy, intensity variability and SRD were predictive for tumour dimension. Regarding 18F-FDG PET, intensity variability, SZV, homogeneity, SUVmax and MTV were predictive for tumour dimension. Four principal components were extracted from PCA: PC1 correlated with all 18F-FDG variables, while PC2, PC3 and PC4 with 68Ga-DOTATOC variables. PC1 was the only significantly predicting angioinvasion (P = 0.0222); PC4 was the only one significantly predicting lymph nodal involvement (P = 0.0151). All principal components except PC4 significantly predicted tumour dimension (P <0.0001 for PC1, P = 0.0016 for PC2 and P < 0.0001 for PC3). Co-V from 68Ga-DOTATOC PET/CT was predictive of the outcome. Conclusion Specific texture features derived from preoperative 68Ga-DOTATOC and 18F-FDG PET/CT could noninvasively predict specific tumour characteristics and patients' outcome, delineating the potential role of dual tracer technique and texture analysis in the risk assessment of patients

This paper investigates the impact of introducing universal free formal personal care on informal caregiving behaviour in Scotland – in particular, we explore the extent to which free formal care might crowd out the supply of informal care. We estimate, in a difference-in-differences framework, that such a reform would: reduce the probability of co-residential informal caregiving (usually, provided by spouses) by around 18% and, conditional on co-residential caring, reduce such informal care by 1.3 hours per week. These estimates suggest that an additional hour of formal care displaces approximately 1 hour of such informal care. However, we find no displacement effect on extra-residential informal caring (often supplied by adult daughters). We also find evidence of increases in labour market participation and hours worked.

Automatic analysis toolboxes are popular in brain image analysis, both in clinical and in preclinical practices. In this regard, we proposed a new toolbox for mouse PET–CT brain image analysis including a new Statistical Parametric Mapping-based template and a pipeline for image registration of PET–CT images based on CT images. The new templates is compatible with the common coordinate framework (CCFv3) of the Allen Reference Atlas (ARA) while the CT based registration step allows to facilitate the analysis of mouse PET–CT brain images. From the ARA template, we identified 27 volumes of interest that are relevant for in vivo imaging studies and provided binary atlas to describe them. We acquired 20 C57BL/6 mice with [18F]FDG PET–CT, and 12 of them underwent 3D T2-weighted high-resolution MR scans. All images were elastically registered to the ARA atlas and then averaged. High-resolution MR images were used to validate a CT-based registration pipeline. The resulting method was applied to a mouse model of Parkinson’s disease subjected to a test–retest study (n = 6) with the TSPO-specific radioligand [18F]VC701. The identification of regions of microglia/macrophage activation was performed in comparison to the Ma and Mirrione template. The new toolbox identified 11 (6 after false discovery rate adjustment, FDR) brain sub-areas of significant [18F]VC701 uptake increase versus the 4 (3 after FDR) macro-regions identified by the Ma and Mirrione template. Moreover, these 11 areas are functionally connected as found by applying the Mouse Connectivity tool of ARA. In conclusion, we developed a mouse brain atlas tool optimized for PET–CT imaging analysis that does not require MR. This tool conforms to the CCFv3 of ARA and could be applied to the analysis of mouse brain disease models.

Background: Tumor heterogeneity poses major clinical challenges in high-grade gliomas (HGGs). Quantitative radiomic analysis with spatial tumor habitat clustering represents an innovative, non-invasive approach to represent and quantify tumor microenvironment heterogeneity. To date, habitat imaging has been applied mainly on conventional magnetic resonance imaging (MRI), although virtually extendible to any imaging modality, including advanced MRI techniques such as perfusion and diffusion MRI as well as positron emission tomography (PET) imaging. Objectives: This study aims to evaluate an innovative PET and MRI approach for assessing hypoxia, perfusion, and tissue diffusion in HGGs and derive a combined map for clustering of intra-tumor heterogeneity. Materials and Methods: Seventeen patients harboring HGGs underwent a pre-operative acquisition of MR perfusion (PWI), Diffusion (dMRI) and 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET imaging to evaluate tumor vascularization, cellularity, and hypoxia, respectively. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast images, and voxel-wise clustering of each quantitative imaging map identified eight combined PET and physiologic MRI habitats. Habitats’ spatial distribution, quantitative features and histopathological characteristics were analyzed. Results: A highly reproducible distribution pattern of the clusters was observed among different cases, particularly with respect to morphological landmarks as the necrotic core, contrast-enhancing vital tumor, and peritumoral infiltration and edema, providing valuable supplementary information to conventional imaging. A preliminary analysis, performed on stereotactic bioptic samples where exact intracranial coordinates were available, identified a reliable correlation between the expected microenvironment of the different spatial habitats and the actual histopathological features. A trend toward a higher representation of

Deep learning (DL) strategies applied to magnetic resonance (MR) images in positron emission tomography (PET)/MR can provide synthetic attenuation correction (AC) maps, and consequently PET images, more accurate than segmentation or atlas-registration strategies. As first objective, we aim to investigate the best MR image to be used and the best point of the AC pipeline to insert the synthetic map in. Sixteen patients underwent a 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and a PET/MR brain study in the same day. PET/CT images were reconstructed with attenuation maps obtained: (1) from CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with a 2D UNet trained on MR image/attenuation map pairs. As for MR, T1-weighted and Zero Time Echo (ZTE) images were considered; as for attenuation maps, CTs and 511 keV low-resolution attenuation maps were assessed. As second objective, we assessed the ability of DL strategies to provide proper AC maps in presence of cranial anatomy alterations due to surgery. Three 11C-methionine (METH) PET/MR studies were considered. PET images were reconstructed with attenuation maps obtained: (1) from diagnostic coregistered CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with 2D UNets trained on the sixteen FDG anatomically normal patients. Only UNets taking ZTE images in input were considered. FDG and METH PET images were quantitatively evaluated. As for anatomically normal FDG patients, UNet AC models generally provide an uptake estimate with lower bias than atlas-based or segmentation-based methods. The intersubject average bias on images corrected with UNet AC maps is always smaller than 1.5%, except for AC maps generated on too coarse grids. The intersubject bias variability is the lowest (always lower than 2%) for UNet AC maps coming from ZTE images, larger for other methods. UNet models working on MR ZTE images and generating synthetic CT or 511 k