About the Special Issue

Dear Colleagues,

The integration of artificial intelligence (AI) in tumor radiotherapy represents a transformative advancement in oncology, offering the potential to enhance treatment precision, improve patient outcomes, and revolutionize clinical workflows. Radiotherapy is a cornerstone of cancer treatment, but its effectiveness can be limited by factors such as tumor heterogeneity, patient-specific variations, and complex treatment planning. AI technologies are rapidly being harnessed to address these challenges and extend the capabilities of radiotherapy through innovations in biomarker discovery and prognosis.

This special issue, “Artificial Intelligence Technology in Tumor Radiotherapy”, focuses on the latest advancements and applications of AI in optimizing radiotherapy for cancer treatment. Topics include AI-driven image analysis, treatment planning, dose prediction, and real-time monitoring of tumor response. In addition, the issue explores the role of AI in biomarker discovery, enabling the identification of novel molecular and imaging biomarkers to predict treatment response and disease progression. The use of AI for prognosis and risk stratification further enhances personalized treatment strategies, tailoring therapies to individual patient profiles.

By leveraging large-scale clinical, imaging, and molecular data, AI methods can deliver more accurate treatment plans, reduce human error, and improve the prediction of outcomes. This special issue aims to bridge the gap between AI-driven research and its practical applications in clinical radiotherapy. Contributions from researchers, clinicians, and industry experts will demonstrate the potential of AI to improve the precision, safety, and efficacy of tumor radiotherapy, advancing cancer care and enhancing survival outcomes. As AI continues to evolve, its integration in radiotherapy and associated biomarker-driven approaches promises a paradigm shift in the fight against cancer.

Keywords: Artificial intelligence, machine learning, medical image analysis, biomedical signal processing, tumor radiotherapy, biomarker discovery, prognosis, prediction, treatment optimization, dose prediction, risk stratification, personalized oncology

Call for Papers