Prostate cancer is one of the most common malignant neoplasms in men worldwide, and is the fifth cause of cancer-related death. In recent years, a new generation of therapies have been approved for the management of metastatic disease. Moreover, the development of new immunotherapeutic drugs has become a novel frontier for the treatment of several tumor types; to date, numerous studies have investigated their potential activity, including in prostate cancer. In this article, we discuss the role of emerging immunotherapeutic drugs in prostate cancer patients.

Prostate cancer (PCa) risk categorization based on clinical/PSA testing results in a substantial number of men being overdiagnosed with indolent, early-stage PCa. Clinically non-significant PCa is characterized as the presence of ISUP grade one, where PCa is found in no more than two prostate biopsy cores.MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET have been proposed as tools to predict ISUP grade one patients and consequently reduce overdiagnosis. In this study, Radiomics analysis is applied to MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET maps to quantify tumor characteristics and predict histology-proven ISUP grades. ICC was applied with a threshold of 0.6 to assess the features’ stability with variations in contouring. Logistic regression predictive models based on imaging features were trained on 31 lesions to differentiate ISUP grade one patients from ISUP two+ patients. The best model based on [⁶⁸Ga]Ga-PSMA-11 PET returned a prediction efficiency of 95% in the training phase and 100% in the test phase whereas the best model based on MRI-ADC had an efficiency of 100% in both phases. Employing both imaging modalities, prediction efficiency was 100% in the training phase and 93% in the test phase. Although our patient cohort was small, it was possible to assess that both imaging modalities add information to the prediction models and show promising results for further investigations.

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWI_b2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWI_b2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWI_b2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWI_b2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWI_b2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

Neuroendocrine tumors (NETs) are rare neoplasms frequently characterized by an upregulation of the mammalian rapamycin targeting (mTOR) pathway resulting in uncontrolled cell proliferation. The mTOR pathway is also involved in skeletal muscle protein synthesis and in adipose tissue metabolism. Everolimus inhibits the mTOR pathway, resulting in blockade of cell growth and tumor progression. The aim of this study is to investigate the role of body composition indexes in patients with metastatic NETs treated with everolimus. The study population included 30 patients with well-differentiated (G1-G2), metastatic NETs treated with everolimus at the IRCCS Romagnolo Institute for the Study of Tumors (IRST) “Dino Amadori”, Meldola (FC), Italy. The body composition indexes (skeletal muscle index [SMI] and adipose tissue indexes) were assessed by measuring on a computed tomography (CT) scan the cross-sectional area at L3 at baseline and at the first radiological assessment after the start of treatment. The body mass index (BMI) was assessed at baseline. The median progression-free survival (PFS) was 8.9 months (95% confidence interval [CI]: 3.4–13.7 months). The PFS stratified by tertiles was 3.2 months (95% CI: 0.9–10.1 months) in patients with low SMI (tertile 1), 14.2 months (95% CI: 2.3 months-not estimable [NE]) in patients with intermediate SMI (tertile 2), and 9.1 months (95% CI: 2.7 months-NE) in patients with high SMI (tertile 3) (p = 0.039). Similarly, the other body composition indexes also showed a statistically significant difference in the three groups on the basis of tertiles. The median PFS was 3.2 months (95% CI: 0.9–6.7 months) in underweight patients (BMI ≤ 18.49 kg/m²) and 10.1 months (95% CI: 3.7–28.4 months) in normal-weight patients (p = 0.011). There were no significant differences in terms of overall survival. The study showed a correlation between PFS and the body composition indexes in patients with NETs treated with everolimus, underlining the role of adipose and muscle tissue in these patients.