AUTHOR=Cai Chengfeng , Hu Wenhui , Zhou Haimei , Zhang Xian , Ren Rongfei , Liu Yilin , Ye Facui 

TITLE=Artificial intelligence−assisted radiation imaging pathways for distinguishing uterine fibroids and malignant lesions in patients presenting with cancer pain: a literature review

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1621642

DOI=10.3389/fonc.2025.1621642

ISSN=2234-943X

ABSTRACT=Uterine fibroids (leiomyomas) are the most common benign uterine tumours, affecting a significant portion of women, and often present with symptoms similar to malignant tumours, such as leiomyosarcoma or endometrial carcinoma, particularly in patients with cancer-related pelvic pain. Conventional imaging modalities, including ultrasound, CT, and MRI, struggle to differentiate between these benign and malignant conditions, often leading to misdiagnoses with potentially severe consequences, such as unnecessary hysterectomies or inadequate treatment for malignancy. Recent advances in artificial intelligence (AI) have begun to address these challenges by enhancing diagnostic accuracy and workflow efficiency. AI-assisted imaging, encompassing techniques like radiomics, convolutional neural networks (CNNs), and multimodal fusion, has demonstrated substantial improvements in distinguishing between uterine fibroids and malignant smooth-muscle tumours. Furthermore, AI has streamlined clinical workflows, enabling faster, more accurate segmentation, and automating decision-making processes, which significantly benefits patients presenting with acute cancer-related pain. Throughout this article the term radiation imaging is used as an umbrella for ionising-based modalities (CT, PET/CT) and non-ionising, radiation-planned modalities such as MRI and diagnostic ultrasound that feed the same radiotherapy or interventional planning pipelines; with that definition clarified, the review synthesizes current developments in AI-assisted radiation imaging for differentiating uterine fibroids from malignant lesions, exploring diagnostic gaps, emerging AI frameworks, and their integration into clinical workflows. By addressing the technical, regulatory, and operational aspects of AI deployment in pelvic-pain management, this review aims to provide a comprehensive roadmap for incorporating AI into personalized, efficient, and equitable oncologic care for women.