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.

Background: Liquid biopsy offers a minimally invasive approach for cancer detection and monitoring, but its diagnostic performance is often limited by low signal abundance and biological noise. Artificial intelligence (AI) has been proposed as a computational framework to integrate heterogeneous and biologically noisy circulating biomarker data; however, the biological underpinnings, clinical relevance, and translational meaning of the reported performance gains remain unclear. Methods: We conducted a systematic review and random-effects meta-analysis of studies published between 2022 and 2025 applying AI-based methods to liquid-biopsy data for oncologic diagnosis. Extracted outcomes included AUROC, sensitivity, specificity, cancer type, analyte class, and AI model. Pooled AUROC and absolute AUROC differences between AI-based and non-AI approaches were estimated with 95% confidence intervals using a random-effects model. AUROC values were logit-transformed prior to pooling to stabilize variance and were subsequently back-transformed for presentation. Statistical heterogeneity was quantified using the I² statistic. Results: Twenty-eight studies across multiple tumor types met inclusion criteria, of which ten provided extractable data for quantitative synthesis. AI-enhanced liquid biopsy achieved a pooled AUROC of 0.924 (95% CI, 0.879–0.953). When head-to-head comparisons were available, AI-based models demonstrated an absolute AUROC improvement of 0.025 (95% CI, 0.019–0.030) compared with conventional analytical approaches. While numerically modest, this improvement was consistent across cancer types and reflects AI’s ability to extract complementary diagnostic signal from complex circulating biomarkers. Between-study heterogeneity was substantial (I² = 88.8%). Conclusions: AI-enhanced liquid biopsy achieved a pooled AUROC of 0.924 (95% CI, 0.879–0.953), reflecting AI’s ability to integrate multiple weak and complementary biological signals rather than reliance on single circulating biomarkers. These findings support AI’s potential role as a clinical decision- support tool in molecular diagnostics; however, translation into practice will require prospectively validated, clinically calibrated models aligned with specific diagnostic intents. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/ , identifier CRD420251163071.

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.

Purpose: Myxoid liposarcoma (MLPS) is a malignant tumor that occurs predominantly in the deep soft tissues of the extremities. Preoperative radiotherapy (RT) is used to reduce tumor volume to achieve adequate surgical margins. This systematic review aims to evaluate the impact of preoperative RT on surgical margins, local recurrence (LR) rates, metastasis development, and overall survival in patients with MLPS and associated prognostic factors. Methods: A systematic literature search was conducted by two reviewers following PRISMA guidelines on PubMed, Scopus, and the Cochrane Library on 30 November 2024. We included prospective and retrospective cohort studies published in English that evaluate surgical margin status, LR and metastasis rates, and survival outcomes in patients undergoing surgical excision of MLPS following neoadjuvant radiotherapy. Two authors extracted tumor characteristics, percentage of round cells (RCs), change in tumor volume post-RT, surgical margins, postoperative complications, LR and metastasis rates, survival rates, and related prognostic factors. Results: The twelve studies included in this review involved 1483 patients with a mean age of 44.8 years. Tumors were mostly located in the lower limbs, deeply localized, and larger than 5 cm in most cases. The average LR and metastasis rates were 5.2% and 17%, respectively. The mean 5-year and 10-year overall survival rates were 87% and 74%, respectively. Poor prognosis was associated with >5% RC components, tumors larger than 15 cm, deep localization, and inadequate surgical margins. Conclusion: The management of MLPS requires a multidisciplinary approach. Preoperative radiotherapy offers several advantages in reducing tumor volume and facilitating the achievement of adequate surgical margins, finally improving local control and long-term outcomes.