About this Research Topic
The integration of artificial intelligence (AI) in the radiotherapy of pelvic and abdominal malignancies represents a transformative advancement in oncology. AI's capability to enhance precision in treatment planning, optimize radiation dosage, and predict treatment outcomes has shown great promise in improving patient care. This special issue explores the potential of AI-assisted radiotherapy in managing malignancies such as cervical, ovarian, colorectal, and prostate cancers.
Contributions are sought from experts in medical physics, radiation oncology, computer science, and related fields who have been at the forefront of integrating AI into clinical practice. Researchers from academic institutions, cancer centers, and industry leaders developing AI tools for oncology are encouraged to share their findings and insights. We particularly welcome studies that address the clinical implementation, challenges, and future directions of AI in radiotherapy for pelvic and abdominal tumors.
The primary goal of this special issue is to explore the transformative role of AI in enhancing the precision, effectiveness, and outcomes of radiotherapy for pelvic and abdominal malignancies. By bringing together cutting-edge research, clinical experiences, and technological advancements, this issue aims to provide a comprehensive overview of AI's impact on treatment planning, adaptive radiotherapy, and patient management. We seek to highlight innovative applications of AI in improving radiation targeting, reducing treatment toxicity, and enabling personalized care for cancers such as cervical, ovarian, colorectal, and prostate. Additionally, the issue will address current challenges, ethical considerations, and future directions, aiming to foster collaboration and inspire further research in AI-assisted oncology.
We invite submissions of original research, methods, reviews, mini-reviews, perspectives, clinical trials, and brief research reports. The specific potential areas of clinical research include, but are not limited to:
• AI in Treatment Planning: Innovations in AI-driven algorithms for enhancing treatment precision, including contouring, dose optimization, and adaptive radiotherapy.
• AI in Image-Guided Radiotherapy (IGRT): Applications of AI in improving imaging techniques, target localization, and real-time treatment adjustments.
• Predictive Analytics and Outcome Prediction: Utilizing AI for predicting patient outcomes, treatment responses, and potential side effects, focusing on personalized treatment strategies.
• Clinical Implementation and Case Studies: Real-world examples and studies on the integration of AI into clinical practice, including workflow optimization and multidisciplinary collaboration.
• Ethical Considerations and Regulatory Challenges: Addressing the ethical, legal, and regulatory aspects of AI in oncology and precision medicine, particularly in patient data management and decision-making processes.
• Future Directions and Emerging Technologies: Exploring upcoming advancements in AI that may revolutionize radiotherapy, such as machine learning, deep learning, and big data analytics.
Contributions are sought from experts in medical physics, radiation oncology, computer science, and related fields who have been at the forefront of integrating AI into clinical practice. Researchers from academic institutions, cancer centers, and industry leaders developing AI tools for oncology are encouraged to share their findings and insights. We particularly welcome studies that address the clinical implementation, challenges, and future directions of AI in radiotherapy for pelvic and abdominal tumors.
The primary goal of this special issue is to explore the transformative role of AI in enhancing the precision, effectiveness, and outcomes of radiotherapy for pelvic and abdominal malignancies. By bringing together cutting-edge research, clinical experiences, and technological advancements, this issue aims to provide a comprehensive overview of AI's impact on treatment planning, adaptive radiotherapy, and patient management. We seek to highlight innovative applications of AI in improving radiation targeting, reducing treatment toxicity, and enabling personalized care for cancers such as cervical, ovarian, colorectal, and prostate. Additionally, the issue will address current challenges, ethical considerations, and future directions, aiming to foster collaboration and inspire further research in AI-assisted oncology.
We invite submissions of original research, methods, reviews, mini-reviews, perspectives, clinical trials, and brief research reports. The specific potential areas of clinical research include, but are not limited to:
• AI in Treatment Planning: Innovations in AI-driven algorithms for enhancing treatment precision, including contouring, dose optimization, and adaptive radiotherapy.
• AI in Image-Guided Radiotherapy (IGRT): Applications of AI in improving imaging techniques, target localization, and real-time treatment adjustments.
• Predictive Analytics and Outcome Prediction: Utilizing AI for predicting patient outcomes, treatment responses, and potential side effects, focusing on personalized treatment strategies.
• Clinical Implementation and Case Studies: Real-world examples and studies on the integration of AI into clinical practice, including workflow optimization and multidisciplinary collaboration.
• Ethical Considerations and Regulatory Challenges: Addressing the ethical, legal, and regulatory aspects of AI in oncology and precision medicine, particularly in patient data management and decision-making processes.
• Future Directions and Emerging Technologies: Exploring upcoming advancements in AI that may revolutionize radiotherapy, such as machine learning, deep learning, and big data analytics.
Keywords: Radiotherapy, Artificial Intelligence, Reproductive System, Digestive System, Malignant Tumors
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
