Background: Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases. Methods: We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies. Results: We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney. Conclusions: Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved.

Immune checkpoint inhibitors (ICIs) are largely used in the treatment of patients with advanced non-small-cell lung cancer (NSCLC). Novel biomarkers that provide biological information that could be useful for clinical management are needed. In this respect, extracellular vesicles (EV)-associated microRNAs (miRNAs) that are the principal vehicle of intercellular communication may be important sources of biomarkers. We analyzed the levels of 799 EV-miRNAs in the pretreatment plasma of 88 advanced NSCLC patients who received anti-PD-1 therapy as single agent. After data normalization, we used a two-step approach to identify candidate biomarkers associated to both objective response (OR) by RECIST and longer overall survival (OS). Univariate and multivariate analyses including known clinicopathologic variables and new findings were performed. In our cohort, 24/88 (27.3%) patients showed OR by RECIST. Median OS in the whole cohort was 11.5 months. In total, 196 EV-miRNAs out 799 were selected as expressed above background. After multiplicity adjustment, abundance of EV-miR-625-5p was found to be correlated with PD-L1 expression and significantly associated to OR by RECIST (p = 0.0366) and OS (p = 0.0031). In multivariate analysis, PD-L1 staining and EV-miR-625-5p levels were constantly associated to OR and OS. Finally, we showed that EV-miR-625-5p levels could discriminate patients with longer survival, in particular in the class expressing PD-L1 ≥50%. EV-miRNAs represent a source of relevant biomarkers. EV-miR-625-5p is an independent biomarker of response and survival in ICI-treated NSCLC patients, in particular in patients with PD-L1 expression ≥50%.

Immunotherapy has revolutionized the treatment paradigm of non-small cell lung cancer and improved patients’ prognosis. Immune checkpoint inhibitors have quickly become standard frontline treatment for metastatic non-oncogene addicted disease, either as a single agent or in combination strategies. However, only a few patients have long-term benefits, and most of them do not respond or develop progressive disease during treatment. Thus, the identification of reliable predictive and prognostic biomarkers remains crucial for patient selection and guiding therapeutic choices. In this review, we provide an overview of the current strategies, highlighting the main clinical challenges and novel potential biomarkers.

The CDK4/6 inhibitors (CDKi) palbociclib, ribociclib, and abemaciclib are currently approved in combination with anti-estrogen therapy for the treatment of advanced and/or metastatic hormone receptor-positive/HER2-neu-negative breast cancer patients. Given the high incidence of bone metastases in this population, we investigated and compared the potential effects of palbociclib, ribociclib, and abemaciclib on the breast cancer bone microenvironment. Primary osteoclasts (OCs) and osteoblasts (OBs) were obtained from human monocyte and mesenchymal stem cells, respectively. OC function was evaluated by tartrate-resistant acid phosphatase assay and real-time PCR; OB activity was assessed by an alizarin red assay. OB/breast cancer co-culture models were generated via the seeding of MCF-7 cells on a layer of OBs, and tumor cell proliferation was analyzed using flow cytometry. Here, we showed that ribociclib, palbociclib, and abemaciclib exerted similar inhibitory effects on the OC differentiation and expression of bone resorption markers without affecting OC viability. On the other hand, the three CDKi did not affect the ability of OB to produce bone matrix, even if the higher doses of palbociclib and abemaciclib reduced the OB viability. In OB/MCF-7 co-culture models, palbociclib demonstrated a lower anti-tumor effect than ribociclib and abemaciclib. Overall, our results revealed the direct effects of CDKi on the tumor bone microenvironment, highlighting differences potentially relevant for clinical practice.

Circulating tumor cells (CTCs) represent a powerful, minimally invasive window into tumor biology and disease evolution. Technological progress over the past decade has markedly improved the ability to isolate, preserve, and interrogate viable CTCs, transforming them from simple prognostic markers to functional tools for precision oncology. Advances in microfluidic platforms, immunomagnetic enrichment, aptamer-based capture, and nanostructured interfaces have expanded the efficiency and fidelity of CTC recovery, enabling comprehensive molecular profiling and ex vivo analysis. These innovations have paved the way for the development of CTC-derived preclinical models, including xenografts, organoids, and chorioallantoic membrane assays, which recapitulate patient-specific tumor heterogeneity and support individualized drug-sensitivity testing. In this review, we summarize current technologies for CTC isolation, outline recent achievements in functional and pharmacological characterization, and discuss the translational impact of CTC-derived models. We further identify persistent challenges and emerging opportunities, highlighting how integration of multi-omics platforms, artificial intelligence, and standardized workflows may accelerate the clinical implementation of CTC-guided personalized therapy.

Background/Objectives: Despite advances in immunotherapy, reliable biomarkers beyond PD-L1 expression are urgently needed to optimize treatment decisions in advanced non-squamous NSCLC. Thyroid Transcription Factor-1 (TTF-1), a biomarker associated with favorable prognosis in chemotherapy-treated patients, has unclear prognostic implications in the immunotherapy era. Methods: We conducted a multicenter retrospective study involving 163 advanced non-squamous NSCLC patients treated with first-line immunotherapy or chemo-immunotherapy and an additional historical chemotherapy-only cohort (n = 37). We evaluated the prognostic significance of TTF-1 expression for progression-free survival (PFS) and overall survival (OS). A systematic review and meta-analysis, performed following PRISMA guidelines, integrated our findings with existing evidence. Hazard ratios (HRs) were calculated using Cox proportional hazards models. Results: TTF-1 negativity was associated with significantly worse median PFS (6.7 vs. 16 months; HR 2.22, 95% CI 1.59–3.13; p < 0.001) and OS (11.5 vs. 26.4 months; HR 2.33, 95% CI 1.64–3.45; p < 0.001) compared to TTF-1 positivity. The prognostic value of TTF-1 was independent of PD-L1 status, with limited predictive relevance of PD-L1 expression observed within TTF-1-negative tumors. Meta-analysis (9 studies, 14 cohorts, n = 2019 patients) confirmed significantly inferior outcomes for TTF-1-negative patients across multiple immunotherapy-based regimens (pooled HR for PFS: 1.75, 95% CI 1.50–2.04; OS: 1.76, 95% CI 1.45–2.14). Conclusions: TTF-1 negativity independently predicts poor prognosis in advanced non-squamous NSCLC treated with immunotherapy-based regimens, identifying patients with limited benefit despite high PD-L1 expression. Integrating TTF-1 status into clinical practice may guide personalized treatment strategies, highlighting the need for prospective validation.