Radiotherapy remains a foundational treatment for malignant tumors, but challenges persist in predicting outcomes and optimizing dosages for individual patients. Variability in responses complicates effective treatment administration, necessitating more precise methodologies. This is where Artificial Intelligence (AI), especially through deep learning models, is stepping in to transform prognosis and treatment strategies. By analyzing extensive clinical, radiological, and genomic databases, AI identifies patterns not easily noticed by human clinicians. These models can predict treatment success, estimate the potential for adverse events, and optimize radiation dosages based on individual patient characteristics. Furthermore, AI-powered approaches can refine tumor response assessment during and after treatment, enabling real-time adjustments and minimizing the risks of overtreatment or undertreatment. The exploration of radiotherapy for malignant tumors, enhanced by AI, could lead to significant improvements in both the efficacy and safety of cancer treatment.
This Research Topic aims to delve into the impact of Artificial Intelligence in improving the prognostic assessment and optimization of radiotherapy for malignant tumors. By leveraging AI technologies, we aim to advance the personalization of treatment plans, optimize radiation dosages, and ultimately improve patient outcomes. Focus areas include increasing predictive accuracy through AI models, real-time tumor response monitoring, and advanced radiotherapy planning. The integration of AI with existing technologies such as radiomics, along with comprehensive clinical and genetic data analysis, allows for more precise treatments tailored to individual patients, reducing treatment-related risks and improving overall treatment efficacy.
To gather further insights into this rapidly evolving field, we welcome contributions that address, but are not limited to, the following themes:
1. The use of AI in predicting treatment outcomes for patients undergoing radiotherapy for various malignancies.
2. Advances in radiomics and the integration of imaging, clinical, and genetic data for personalized treatment planning.
3. Exploration of AI-driven models for radiotherapy dose optimization based on patient-specific factors.
4. The role of AI in assessing tumor response and monitoring for early recurrence or metastasis during treatment.
5. Exploration of innovative radiotherapy techniques enhanced by AI.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Brief Research Report
- Case Report
- Clinical Trial
- Editorial
- General Commentary
- Hypothesis and Theory
- Methods
- Mini Review
- Opinion
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: Artificial Intelligence, Machine Learning, Radiotherapy, Malignant Tumors, Dosages
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.