Prostate-specific membrane antigen-PET/CT may result in stage migration in prostate cancer: performances, quantitative analysis, and potential criticism in the clinical practice.

AIM: The early detection of prostate cancer (PCa) metastatic disease with PET imaging leads to stage migration and change of disease management. We aimed to assess the impact on clinical management deriving from prostate-specific membrane antigen (PSMA) imaging with a digital PET/CT during the routine application in the staging and restaging process of PCa. MATERIAL AND METHODS: Eighty consecutive PCa patients underwent 18F-PSMA-1007. Digital PET/CT were retrospectively evaluated and discussed with oncologists to evaluate the impact on clinical management. Performances analysis, correlation among variables also considering semiquantitative parameters have been conducted. RESULTS: In the whole group of 80 patients at staging (N = 31) and restaging (N = 49), the detection rate of PSMA PET was 85% for all lesions. At staging, the performance analysis resulted in sensitivity 77.6%, specificity 89.5%, negative predictive value (NPV) 77.6%, positive predictive value (PPV) 89.5%, accuracy 85.7%, and area under curve (AUC) 0.87%. The performance of restaging PET in the group of patients with PSA values <1 ng/ml resulted in the following values: sensitivity 66.7%, specificity 92.9%, NPV 85.7%, PPV 81.3%, accuracy 82.6%, and AUC 0.79. Semiquantitative analysis revealed a mean value of SUVmax, metabolic tumor volume, and total lesion PSMA expression with differences in patients with high risk compared to low intermediate. At restaging PET, semiquantitative values of patients with total prostate specific antigen (tPSA) ≤ 1 ng/ml were significantly less than those of the tPSA > 1 ng/ml. A significant impact on clinical management was reported in 46/80 patients (57.5%) based on PSMA PET findings at staging and restaging. CONCLUSION: Although PSMA-PET provides optimal performances, its current role in redefining a better staging should be translated in the current clinical scenario about potential improvement in clinical/survival outcomes.

Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.

The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).