Objective To summarize the new progress of circulating tumor cell markers and their clinical application. Methods The related literatures about the detection and clinical application of circulating tumor cell markers in recent years were reviewed. Resuts Epithelial markers, other markers such as human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and immuno-checkpoint genes have also become a useful means to detect the ability of malignant metastasis of circulating tumor cells (CTCs). Moreover, the epithelial-to-mesenchymal transition (EMT) and cancer stem cells (CSCs) have also received increasing attention as important CTCs markers owing to their roles in the biological progression of metastasis. Conclusions Through the detection of peripheral blood CTCs, to identify early cancer, monitoring the metastasis and recurrence of cancer and its treatment on all has the very good guiding significance. Signs of circulating tumor cells has great clinical application value in diagnosis and treatment of tumor and prognosis.
ObjectiveTo systematically summarize recent advancements in the application of artificial intelligence (AI) in key components of radiotherapy, explore the integration of technical innovations with clinical practice, and identify current limitations in real-world implementation. MethodsA comprehensive analysis of representative studies from recent years was conducted, focusing on the technical implementation and clinical effectiveness of AI in image reconstruction, automatic delineation of target volumes (TV) and organs at risk (OAR), intelligent treatment planning, and prediction of radiotherapy-related toxicities. Particular attention was given to deep learning models, multimodal data integration, and their roles in enhancing decision-making processes. ResultsAI-based low-dose image enhancement techniques had significantly improved image quality. Automated segmentation methods had increased the efficiency and consistency of contouring. Both knowledge-driven and data-driven planning systems had addressed the limitations of traditional experience-dependent approaches, contributing to higher quality and reproducibility in treatment plans. Additionally, toxicity prediction models that incorporate multimodal data enable more accurate, personalized risk assessment, supporting safer and more effective individualized radiotherapy. ConclusionsRadiotherapy is a fundamental modality in cancer treatment. However, achieving precise tumor ablation while minimizing damage to surrounding healthy tissues remains a significant challenge. AI has demonstrated considerable value across multiple technical stages of radiotherapy, enhancing precision, efficiency, and personalization. Nevertheless, challenges such as limited model generalizability, lack of data standardization, and insufficient clinical validation persist. Future work should emphasize the alignment of algorithmic development with clinical demands to facilitate the standardized, reliable, and practical application of AI in radiotherapy.