Magnetic induction hyperthermia becomes a very important tumor treatment method at present. In order to ensure a successful operation, doctors should make hyperthermia treatment planning before surgery. Based on Integration Healthcare Enterprise (IHE) framework and Digital Imaging and Communications in Medcine (DICOM) standard, we proposed and carried out a network workflow integrated with modern medical information systems for the dissemination of information in magnetic induction hyperthermia like accurate accessing patient information and radiology image data, storing processed images, sharing and verifying hyperthermia reports. The results proved that our system could not only improve the efficiency of magnetic induction hyperthermia treatment planning, but also save medical resources and reduce labor costs.
Intensity-modulated radiotherapy planning for nasopharyngeal carcinoma is very complex. The quality of plan is often closely linked to the experience of the treatment planner. In this study, 10 nasopharyngeal carcinoma patients at different stages were enrolled. Based on the scripting of Pinnacle3 9.2 treatment planning system, the computer program was used to set the basic parameters and objective parameters of the plans. At last, the nasopharyngeal carcinoma intensity-modulated radiotherapy plans were completed automatically. Then, the automatical and manual intensity-modulated radiotherapy plans were statistically compared and clinically evaluated. The results showed that there were no significant differences between those two kinds of plans with respect to the dosimetry parameters of most targets and organs at risk. The automatical nasopharyngeal carcinoma intensity-modulated radiotherapy plans can meet the requirements of clinical radiotherapy, significantly reduce planning time, and avoid the influence of human factors such as lack of experience to the quality of plan.
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.