Background: To evaluate the accuracy of 68Ga-prostate specific membrane antigen (PSMA) PET/CT in the diagnosis of clinically significant prostate cancer (csPCa) (Grade Group > 2) in men enrolled in Active Surveillance (AS) protocol. Methods: From May 2013 to May 2021, 173 men with very low-risk PCa were enrolled in an AS protocol study. During the follow-up, 38/173 (22%) men were upgraded and 8/173 (4.6%) decided to leave the AS protocol. After four years from confirmatory biopsy (range: 48–52 months), 30/127 (23.6%) consecutive patients were submitted to mpMRI and 68Ga-PSMA PET/CT scan before scheduled repeated biopsy. All the mpMRI (PI-RADS > 3) and 68Ga-PET/TC standardised uptake value (SUVmax) > 5 g/mL index lesions underwent targeted cores (mpMRI-TPBx and PSMA-TPBx) combined with transperineal saturation prostate biopsy (SPBx: median 20 cores). Results: mpMRI and 68Ga-PSMA PET/CT showed 14/30 (46.6%) and 6/30 (20%) lesions suspicious for PCa. In 2/30 (6.6%) men, a csPCa was found; 68Ga-PSMA-TPBx vs. mpMRI-TPBx vs. SPBx diagnosed 1/2 (50%) vs. 1/2 (50%) vs. 2/2 (100%) csPCa, respectively. In detail, mpMRI and 68Ga-PSMA PET/TC demonstrated 13/30 (43.3%) vs. 5/30 (16.7%) false positive and 1 (50%) vs. 1 (50%) false negative results. Conclusion: 68Ga-PSMA PET/CT did not improve the detection for csPCa of SPBx but would have spared 24/30 (80%) scheduled biopsies showing a lower false positive rate in comparison with mpMRI (20% vs. 43.3%) and a negative predictive value of 85.7% vs. 57.1%, respectively.

The ⁶⁴Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after ⁶⁴Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [⁶⁴Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [⁶⁴Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [⁶⁴Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard template space (3D whole-body Digimouse atlas), and 108 radiomics features were extracted from seven organs (namely, heart, bladder, stomach, liver, spleen, kidney, and lung) to investigate possible changes over time in [⁶⁴Cu]chelator biodistribution. The one-way analysis of variance and post hoc Tukey Honestly Significant Difference test revealed that, while heart, stomach, spleen, kidney, and lung districts showed a very low percentage of radiomics features with significant variations (p-value < 0.05) among the three groups of mice, a large number of features (greater than 60% and 50%, respectively) that varied significantly between groups were observed in bladder and liver, indicating a different in vivo uptake of the ⁶⁴Cu-labeled chelator over time. The proposed methodology may improve the method of calculating the [⁶⁴Cu]chelator biodistribution and open the way towards a decision support system in the field of new radiopharmaceuticals used in preclinical imaging trials.

Meningiomas represent the most common benign histological tumor of the central nervous system. Usually, meningiomas are intracranial, showing a typical dural tail sign on brain MRI with Gadolinium, but occasionally they can infiltrate the skull or be sited extracranially. We present a systematic review of the literature on extracranial meningiomas of the head and neck, along with an emblematic case of primary extracranial meningioma (PEM), which provides further insights into PEM management. A literature search according to the PRISMA statement was conducted from 1979 to June 2021 using PubMed, Web of Science, Google Scholar, and Scopus databases, searching for relevant Mesh terms (primary extracranial meningioma) AND (head OR neck). Data for all patients were recorded when available, including age, sex, localization, histological grading, treatment, possible recurrence, and outcome. A total of 83 published studies were identified through PubMed, Google Scholar, and Scopus databases, together with additional references list searches from 1979 to date. A total of 49 papers were excluded, and 34 manuscripts were considered for this systematic review, including 213 patients. We also reported a case of a 45-year-old male with an extracranial neck psammomatous meningioma with sizes of 4 cm × 3 cm × 2 cm. Furthermore, whole-body ⁶⁸Ga-DOTATOC PET/CT was performed, excluding tumor spread to other areas. Surgical resection of the tumor was accomplished, as well as skin flap reconstruction, obtaining radical removal and satisfying wound healing. PEMs could suggest an infiltrative and aggressive behavior, which has never found a histopathological correlation with a malignancy (low Ki-67, <5%). Whole-body ⁶⁸Ga-DOTATOC PET/CT should be considered in the patient’s global assessment. Surgical removal is a resolutive treatment, and the examination of frozen sections can confirm the benignity of the lesion, reducing the extension of the removal of healthy tissue surrounding the tumor.

⁶⁸Ga-DOTATOC represents a useful tool in tumor contouring for radiosurgery planning. We present a case series of patients affected by meningiomas on who we performed ⁶⁸Ga-DOTATOC positron emission tomography (PET)/CT pre-operatively, a subgroup of which also underwent a post-operative ⁶⁸Ga-DOTATOC PET/CT to evaluate the standardized uptake value (SUV) modification after Gamma Knife ICON treatment in single or hypofractionated fractions. Twenty patients were enrolled/included in this study: ten females and ten males. The median age was 52 years (range 33–80). The median tumor diameter was 3.68 cm (range 0.12–22.26 cm), and the median pre-radiotherapy maximum SUV value was 11 (range 2.3–92). The average of the relative percentage changes between SUVs at baseline and follow up was −6%, ranging from −41% to 56%. The SUV was reduced in seven out of 12 patients (58%), stable in two out of 12 (17%), and increased in three out of 12 (25%), suggesting a biological response of the tumor to the Gamma Knife treatment in most of the cases. ⁶⁸Ga-DOTATOC-PET represents a valuable tool in assessing the meningioma diagnosis for primary radiosurgery; it is also promising for follow-up assessment.

Background: Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness. Aim: We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from ¹⁸F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation. Conclusions: Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68–0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34–0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.

The aim of this study is to investigate the role of [¹⁸F]-PSMA-1007 PET in differentiating high- and low-risk prostate cancer (PCa) through a robust radiomics ensemble model. This retrospective study included 143 PCa patients who underwent [¹⁸F]-PSMA-1007 PET/CT imaging. PCa areas were manually contoured on PET images and 1781 image biomarker standardization initiative (IBSI)-compliant radiomics features were extracted. A 30 times iterated preliminary analysis pipeline, comprising of the least absolute shrinkage and selection operator (LASSO) for feature selection and fivefold cross-validation for model optimization, was adopted to identify the most robust features to dataset variations, select candidate models for ensemble modelling, and optimize hyperparameters. Thirteen subsets of selected features, 11 generated from the preliminary analysis plus two additional subsets, the first based on the combination of robust and fine-tuning features, and the second only on fine-tuning features were used to train the model ensemble. Accuracy, area under curve (AUC), sensitivity, specificity, precision, and f-score values were calculated to provide models’ performance. Friedman test, followed by post hoc tests corrected with Dunn-Sidak correction for multiple comparisons, was used to verify if statistically significant differences were found in the different ensemble models over the 30 iterations. The model ensemble trained with the combination of robust and fine-tuning features obtained the highest average accuracy (79.52%), AUC (85.75%), specificity (84.29%), precision (82.85%), and f-score (78.26%). Statistically significant differences (p < 0.05) were found for some performance metrics. These findings support the role of [¹⁸F]-PSMA-1007 PET radiomics in improving risk stratification for PCa, by reducing dependence on biopsies.