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Published on: April 11, 2016
Multidimensional technological advances in cervical cancer screening: From standardized processes to precision
Xiang Li1, Jiaxin Zheng1, Chang Liu2
1Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang 110042, China; Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, China.
None:
Cervical cancer is a common malignancy among women worldwide. To address this significant public health issue, the World Health Organization launched the "Eliminating Cervical Cancer" initiative. Effective screening of cervical cancer is crucial for reducing its morbidity and mortality. With continuous technological advancements, cervical cancer screening has evolved from traditional cytology and human papillomavirus (HPV) testing models to a new era of multidimensional, multilevel, and precise screening. This comprehensive review focuses on the core progress of screening technology in recent years, including the innovation of traditional screening methods, noninvasive optical imaging, molecular diagnosis from the perspective of precision medicine, and the deep integration and empowerment of artificial intelligence in the entire screening process. Cervical liquid-based cytology, combined with automated cell sorting technology and intelligent whole-slide image analysis, has achieved high-throughput identification of abnormal cells. The accuracy and specificity of HPV and multiomic biomarker detection technologies have significantly improved, providing a new basis for the accurate triage of patients with high-risk infections. Noninvasive cervical imaging technology already offers early identification of cervical precancerous lesions at the molecular level, combining ease of use with good patient acceptance. Notably, artificial intelligence technology is being integrated into multiple screening processes, leveraging horizontal integration and cross-platform capabilities in image recognition, risk assessment, auxiliary diagnosis, and automated testing processes, becoming a key driver of innovation in screening systems.
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