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Updated: Jul 15, 2025
Stereotactic Radiosurgery for Gynecologic Cancer
Published on: April 17, 2012
Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic
Yuting Jiang1, Chengdi Wang1, Shengtao Zhou1
1Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Abstract:
As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.
Insights
Artificial intelligence (AI) offers new opportunities in gynecologic oncology, particularly for cervical, ovarian, and endometrial cancers. This review highlights AI
Area of Science:
- Gynecologic Oncology
- Artificial Intelligence
- Precision Medicine
Background:
- Artificial intelligence (AI) has shown success in various cancer types, but its application in gynecologic oncology is less explored.
- Gynecologic cancers, including cervical, ovarian, and endometrial cancers, represent a significant area for AI-driven advancements.
- There is a need to understand AI's role in managing these specific cancers throughout their entire course.
Purpose of the Study:
- To review the current contributions of AI in gynecologic oncology.
- To explore AI applications in risk stratification and patient management for cervical, ovarian, and endometrial cancers.
- To identify challenges and potential solutions for implementing AI in real-world gynecologic oncology settings.
Main Methods:
- Review of state-of-the-art AI techniques applied to gynecologic oncology.
- Analysis of data sources including electronic health records, various cancer imaging modalities (radiological, colposcopic, cytological, histopathological), and molecular profiling.
- Examination of AI's role in risk stratification and whole-course patient management.
Main Results:
- AI techniques are increasingly contributing to refined risk stratification in gynecologic tumors.
- AI aids in the comprehensive management of patients with cervical, ovarian, and endometrial cancers.
- Information extracted from diverse data sources (clinical, imaging, molecular) is crucial for AI applications.
Conclusions:
- AI presents significant opportunities for advancing gynecologic oncology care.
- Challenges in AI implementation, such as performance validation and real-world deployment, need to be addressed.
- Further research and development are required to overcome limitations and fully realize AI's potential in managing gynecologic cancers.
