Artificial Intelligence in Gynecological Oncology: Advances in Imaging and Therapeutic Strategies

The integration of artificial intelligence (AI) in oncology is rapidly transforming both diagnostic imaging and therapeutic decision-making. From radiomics and image segmentation to predictive modeling and treatment planning, AI has the potential to enhance accuracy, efficiency, and personalization in cancer care. Gynecologic cancers, including endometrial, ovarian, and cervical malignancies, remain a significant global health burden. Early and accurate diagnosis, precise staging, and individualized treatment planning are critical for improving outcomes. The integration of artificial intelligence (AI) into diagnostic imaging and therapeutic workflows is revolutionizing gynecologic oncology.

This Research Topic aims to highlight recent advances in AI-driven imaging analysis and therapeutic decision-making for gynecological cancers. We welcome contributions that explore explore AI-driven innovations in medical imaging modalities such as ultrasound, MRI, and CT for tumor detection, segmentation, and characterization. Submissions addressing radiomics, deep learning, multi-modal data integration, and predictive modeling for prognosis and treatment response are encouraged. We also welcome studies on AI-assisted surgical planning, radiation therapy optimization, and systemic therapy decision support.

The goal of this Research Topic is to address these challenges by highlighting how artificial intelligence (AI) can revolutionize gynecological oncology through improved imaging analysis and therapeutic strategies. By leveraging AI techniques such as deep learning, radiomics, and predictive modeling, we aim to promote research that enhances diagnostic accuracy, risk stratification, and individualized treatment planning. Ultimately, this collection seeks to foster multidisciplinary collaboration, accelerate clinical translation of AI tools, and encourage discussion of ethical and practical considerations to ensure safe and effective implementation. Through these efforts, we hope to contribute to better patient outcomes and advance precision medicine in gynecologic cancer care.

This Research Topic focuses on the application of artificial intelligence (AI) in gynecological oncology, with particular emphasis on imaging and therapeutic strategies for endometrial, ovarian, and cervical cancers. Manuscripts can include original research articles, reviews, perspectives, case reports, and method papers that highlight novel AI applications, clinical validation, translational studies, and discussions on ethical and regulatory challenges. Our aim is to compile a comprehensive and multidisciplinary collection that advances precision medicine and clinical care in gynecological cancers.

We welcome contributions that include, but are not limited to:

• AI-driven tools for imaging acquisition, segmentation, and interpretation

• Radiomics and image-based biomarkers for classification, risk assessment, and prognosis

• Integration of imaging and clinical data for personalized treatment planning

• AI applications in surgical planning, radiation therapy, or systemic therapy

• Real-world clinical validation studies and multi-center collaborations

• Ethical, regulatory, and implementation challenges in deploying AI systems in clinical practice




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
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Specialty Grand Challenge
• Systematic Review
• Technology and Code

Keywords: Artificial integence, Gynecological Cancers, radiomics, MRI, CT, US, PET/CT, ovarian cancer, endometrial cancer, cervical cancer, therapeutic strategies

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.