Purpose: To assess the diagnostic accuracy of Artificial Intelligence as a Medical Device (AIaMD) for cancer classification in clinical settings, according to the PIRT framework? Participants: adults with solid malignant neoplasms of the skin and/or mucosas; Index test: AIaMD; Comparator: conventional unassisted diagnostic methods; Reference standard: histopathology and/or clinical diagnosis; Target condition: skin and mucosal cancer. Methods: Following PRISMA-DTA guidelines, a systematic search of six electronic databases and grey literature was conducted for studies published before 30 March 2026. The protocol was registered in PROSPERO (CRD42025634932). Diagnostic performance metrics (sensitivity and specificity) were extracted or estimated from contingency tables. QUADAS-2 was used to evaluate risk of bias and applicability concerns. Meta-analysis was performed using a bivariate binomial generalized linear mixed model; heterogeneity was assessed with τ2 and 95% prediction intervals, and paired comparison and meta-regression for subgroup analysis were performed. Certainty of evidence was assessed using GRADE-DTA. Results: Among 25,080 retrieved records, 24 studies were included. Overall, 83.3% of the studies were prospective, and 87.5% were related to skin cancer. The meta-analysis included 17 studies for AIaMD and 9 for unassisted clinicians. Findings revealed a pooled sensitivity of 0.893 (95% CI 0.818–0.940, τ2: 1.32) and specificity of 0.749 (95% CI 0.586–0.862, τ2: 2.38) for AIaMD, compared to sensitivity of 0.849 (95% CI 0.751–0.913, τ2: 0.620) and specificity of 0.777 (95% CI 0.624–0.880, τ2: 1.22) for clinicians. In eight paired studies, there was no significant difference in sensitivity (p = 0.74) or specificity (p = 0.85). Twenty-three of 24 (95.8%) studies had at least one domain at high risk of bias after QUADAS-2 evaluation, while GRADE-DTA indicated overall very low certainty of evidence. Conclusions: AIaMD performance was represented by high sensitivity but lower and highly variable specificity. Although clinically relevant differences cannot be excluded, AIaMD demonstrated no statistically significant differences in paired comparisons when compared to clinicians. Nonetheless, methodological limitations, factors influencing bias and applicability, and very low certainty of evidence warrant cautious interpretation and further research before real-world implementation.
Tumour hypoxia significantly influences disease advancement and treatment resistance in oral squamous cell carcinoma (OSCC). Hypoxia-inducible factor-1α (HIF-1α) is preserved when oxygen is lacking and triggers the expression of hypoxia-responsive genes that have roles in angiogenesis, metabolic adaptability, tumour cell viability, invasion, and metastasis. The elevated levels of vascular endothelial growth factor (VEGF) induced by HIF-1α result in atypical angiogenesis. The dysfunction of cell junction proteins and heightened activity of matrix metalloproteinases caused by hypoxia facilitate cancer invasion and metastasis. Hypoxic stress prompts cellular modifications, including autophagy and the preservation of cancer cell populations with enhanced survival capabilities, hence facilitating tumour recurrence and heterogeneity. Hypoxia diminishes the effectiveness of standard chemotherapy and radiotherapy due to decreased drug transport, hindered cell growth, and diminished oxygen-dependent radiation-induced DNA damage. Thus, treatment strategies for the hypoxic tumour microenvironment encompass the suppression of HIF and VEGF, tumour reoxygenation, hypoxia radiosensitizers, and hypoxia-activated lethal compounds. Nonetheless, the diverse and fluctuating characteristics of tumour hypoxia, along with the initiation of compensatory survival mechanisms, continue to pose significant obstacles. Enhanced comprehension of hypoxia-induced molecular networks and the advancement of multimodal, biomarker-directed treatment strategies could improve outcomes in OSCC.
Modern pipelines integrating multi-omics data, chemical libraries, structural biology information and artificial intelligence are currently accelerating drug discovery and precision medicine. Aligned with these advances, the multi-omics profiling technologies enable the identification of unique genetic and epigenetic signatures for stratification of patients based on cancer specific target and vulnerability. Targeted therapies to specific receptor or enzyme mutated or overexpressed often fail because cancer cells can adapt quickly. Tumor cells overgrow driven by new gene mutation and parallel crosstalk of signaling pathways. Patients with cancer and the hereditary syndrome targets such as BRCA1/2, and emerging targets such as ATR, CHK1, WEE1, and POLθ, are hypersensitive to ionizing radiation and PARP inhibitors. Combining synthetic lethality inhibitors, chemotherapy and immunotherapy effectively kill cancer cells by breaking multiple survival signaling pathways. Antibodies to immune checkpoint inhibitors targeting molecules like PD-1, PD-L1, and CTLA-4 can improve survival of patients, though predictive immunotype remains challenging. Emerging pharmacological approaches combining either epigenetic modifiers, oncomicrobiotics or engineered bacteria sequentially convert tumors into a highly antigenic, T cell-inflamed, and checkpoint-responsive tumor, providing a mechanistically grounded framework for next-generation precision immunotherapy. However, large human clinical trials remain necessary to standardize protocols, biomarkers and long-term efficacy. Cancer vaccines have evolved through rational engineering synthetic mRNA encoding tumor-specific antigens or tumor-associated antigens. This will enable the next generation of truly therapeutic and preventive vaccines for many cancers. This review provides a timely synthesis of the basic mechanisms for rational multidrug treatments for cancers.
Prolactin (PRL) is a hormone synthesized by lactotrophs in the anterior pituitary gland. It is also produced by extrapituitary glands such as the prostate and breast, where it promotes growth. Notably, PRL also contributes to cancer development in these glands. PRL is known to stimulate cancer cell migration, suggesting its possible involvement in metastasis, for which cell migration is a critical step. However, the role and mechanisms by which PRL induces cancer cell migration are not yet fully understood. One well-studied mechanism involves its receptor, the prolactin receptor (PRLR). After PRL binds to its receptor, PRLR dimerizes and induces transphosphorylation of Janus kinase 2 (JAK2), which subsequently phosphorylates p21-activated kinase 1 (PAK1), leading to increased cell migration. Although the PRL-JAK2-PAK1 pathway in cancer cell migration has been investigated, the molecular mechanism remains incompletely understood. Additionally, other PRL-activated pathways are implicated in migration, but their roles remain debated. This narrative review examines and discusses the main findings on the involvement of PRL in the migration of cancerous and non-cancerous cells, as well as the molecular mechanisms involved. We conclude that PRL promotes the migration of both cancerous and non-cancerous cell lines and should, therefore, be taken into account in future cancer research.
Background/Objectives: Primary renal NETs are exceptionally rare neoplasms that occur disproportionately in horseshoe kidneys. Their rarity and non-specific clinical and radiological features make preoperative diagnosis particularly challenging. Methods: Herein, we report the case of a 60-year-old woman presenting with a painful left lumbar mass associated with dysuria and pollakiuria. Results: Computed tomography revealed a large heterogeneous tumor arising from the left moiety of a horseshoe kidney. Histopathological examination of the resected specimen demonstrated a 14 cm well-differentiated NET (grade 1) with diffuse chromogranin A and synaptophysin expression, a mitotic count of 1 per 2 mm2, and a Ki-67 proliferation index of <1%. The histopathological and immunohistochemical findings, together with the absence of an extrarenal primary site on staging investigations, supported the diagnosis of a primary renal NET. The postoperative course was uneventful, and the patient remained disease-free after two years of follow-up. Conclusions: This case highlights the diagnostic challenges posed by this rare entity and underscores the importance of including primary renal neuroendocrine tumors in the differential diagnosis of renal masses arising in horseshoe kidneys.
