Background: The treatment of heavily pretreated patients with metastatic renal cell carcinoma (mRCC) represents an unmet medical need and is still challenging. Objectives: The primary objective was to assess the effectiveness of the lenvatinib plus everolimus combination and the secondary objective was the toxicity profile of this combination. Design: We conducted a longitudinal retrospective study examining mRCC patients pre-treated with one or more lines of therapy among different cancer centers in Italy. Methods: The study included patients who received the combination of lenvatinib plus everolimus as either a second-line treatment or beyond. We assessed progression-free survival (PFS), time to treatment failure (TTF), overall survival (OS), response rate (RR), and toxicity profile. In addition, we explored the potential relationship between treatment effectiveness and clinical and laboratory parameters. Results: In all, 33 patients were assessed, the median age was 60 years, 57% had an Eastern Cooperative Oncology Group performance status of 1–2 and. 63% received ⩾ 3 prior lines of therapy. 62% were ‘intermediate risk’ according to the International Metastatic Renal Cell Carcinoma Database Consortium and 30% were ‘poor risk’. The RR was 42% (no complete response), 18% stable disease. Median OS was 11.2 months (95% CI 6.8–19.9), median PFS was 6.7 months (95% CI 0.6–30.8), and median TTF was 6.7 months (95% CI 4.8–16.6). A shorter OS was significantly associated with lymph node metastases ( p = 0.043, 95% CI), neutrophils/ lymphocytes ratio (NLR) ⩾ 3 ( p = 0.007), hemoglobin/red cell distribution width ratio cutoff value <0.7 was significant ( p = 0.03) while a shorter PFS was associated with lung ( p = 0.048) and brain metastases ( p = 0.023). The most frequent G1 toxicity was diarrhea (24%), G2 was fatigue (30%), and hypertension and skin toxicity (6%) for G3. Conclusion: Our findings suggest a clinically relevant effectiveness of lenvatinib plus everolimus combination with an acceptable toxicity profile for heavily pretreated patients with mRCC.

Background: The aim of the present study was to dissect the clinical outcome of GB patients through the integration of molecular, immunophenotypic and MR imaging features. Methods: We enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI sequences were used to perform a manual evaluation of multiple quantitative variables, among which we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS (median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged as the most significant prognostic factor (median OS = 23 months in V-CD4^high vs. 13 months in V-CD4^low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMT^meth GB, while p53^mut appeared to condition a desert immune background. When individual genetic (MGMT^unmeth), MR imaging (mean ADC^low) and TIME (V-CD4+TILs^low) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI 7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion: Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models in GB patients.

Pancreatic ductal adenocarcinoma remains associated with a poor prognosis, even when diagnosed at an early stage. Consequently, it is imperative to carefully consider the available therapeutic options and tailor them based on clinically relevant biomarkers. In our comprehensive review, we specifically concentrated on the identification of novel predictive and prognostic markers that have the potential to be integrated into multiparametric scoring systems. These scoring systems aim to accurately predict the efficacy of neoadjuvant chemotherapy in surgically resectable pancreatic cancer cases. By identifying robust predictive markers, we can enhance our ability to select patients who are most likely to benefit from neoadjuvant chemotherapy. Furthermore, the identification of prognostic markers can provide valuable insights into the overall disease trajectory and inform treatment decisions. The development of multiparametric scoring systems that incorporate these markers holds great promise for optimizing the selection of patients for neoadjuvant chemotherapy, leading to improved outcomes in resectable pancreatic neoplasia. Continued research efforts are needed to validate and refine these markers and scoring systems, ultimately advancing the field of personalized medicine in pancreatic adenocarcinoma management.

Primary central nervous system (CNS) tumors remain a major challenge in neuro-oncology due to their cellular heterogeneity, infiltrative growth, and the protective blood–brain barrier (BBB), which limits the effectiveness of systemic therapies. Despite aggressive multimodal treatments, patient survival remains poor, highlighting the urgent need for new therapeutic strategies. Ultrasound-based therapies, including focused ultrasound (FUS) and sonodynamic therapy (SDT), have emerged as promising approaches. FUS can transiently open the BBB and induce localized mechanical and thermal effects, enhancing drug delivery, while SDT activates tumor-specific sensitizers to generate reactive oxygen species that trigger cancer cell death. Preclinical and early clinical studies suggest that combining these modalities with chemotherapy, immunotherapy, or radiotherapy may improve treatment outcomes. Emerging tools such as AI-guided monitoring, theranostic platforms, and ultrasound-responsive nanoparticles could further enable personalized interventions. However, challenges remain, including protocol variability, tumor heterogeneity, and limited long-term safety data. Careful optimization and clinical validation are needed before these strategies can be widely adopted in the management of CNS tumors.