Early Detection and Novel Diagnostic Approaches in Gynecological Malignancies

Gynecological malignancies, including ovarian, fallopian tube, peritoneal, cervical, and endometrial cancers, remain a major global health burden, largely due to a lack of recognition of symptoms, late-stage diagnosis, and limited effective screening strategies. Early detection is generally associated with improved survival outcomes; however, cervical cancer is the only one for which there is an effective screening method. Despite effective screening programs, participation remains suboptimal in many settings. Other gynecologic cancers may be asymptomatic, or symptoms may not be recognized as related to cancer and lack reliable, widely implemented screening tools. It is important to distinguish between early detection (which encompasses screening, risk prediction, and patient triage) and diagnostic confirmation (which refers to confirming the presence of disease). Recent advances in molecular biology, imaging technologies, and biomarker discovery have opened new avenues for earlier and more accurate detection, preceding definitive diagnostic confirmation. Techniques such as liquid biopsy, next-generation sequencing, and advanced imaging modalities are increasingly being explored to detect malignancies at a preclinical stage. Despite these developments, challenges persist in terms of sensitivity, specificity, cost-effectiveness, and clinical validation.This Research Topic aims to explore innovative strategies and emerging technologies that enhance early detection and diagnosis of gynecological cancers. A central challenge in this field is the identification of reliable, non-invasive, and cost-effective diagnostic tools, which are acceptable to patients, that can be integrated into routine clinical practice. While conventional screening methods such as HPV testing and cytology and imaging have improved outcomes in certain cancers like cervical cancer, there remains a critical need for improved diagnostic approaches, particularly for ovarian and endometrial malignancies.This topic seeks to highlight recent advances in liquid biopsy, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomal biomarkers, as well as improvements in imaging techniques and artificial intelligence-driven diagnostics. Artificial intelligence is rapidly transforming gynecological oncology diagnostics. Machine learning and deep learning algorithms are key to improving detection accuracy in radiological imaging and digital pathology. By integrating multi-modal clinical, imaging, and genomic data, AI-driven approaches offer powerful tools for early risk stratification, automated tumor classification, and patient triage. We strongly encourage contributions that explore the development, clinical validation, and real-world implementation of these AI technologies to support clinical decision-making.Contributions are encouraged to address the development and validation of predictive biomarkers, risk stratification models, and multi-omics approaches. By fostering interdisciplinary collaboration among gynecologic oncologists, gynecologic pathologists, radiologists, and data scientists, this Research Topic aims to accelerate the translation of novel diagnostic tools into clinical settings, ultimately improving early detection and patient survival.This Research Topic welcomes contributions focusing on cutting-edge diagnostic strategies and early detection methods in gynecological malignancies. Submissions may include original research articles, systematic reviews, meta-analyses, and clinical studies.• Liquid biopsy technologies (ctDNA, CTCs, exosomes) in early cancer detection • Advances in imaging techniques, digital pathology, and AI-assisted diagnostics • Discovery and validation of predictive and prognostic biomarkers • Screening strategies and risk stratification models • Multi-omics approaches for early diagnosis We particularly encourage studies that demonstrate clinical applicability, translational relevance, and potential for integration into screening programs.Please note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases that are not supplemented by relevant functional validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic. Topic editors: Eva Chalas, Katarzyna Terlikowska Source: https://www.frontiersin.org/research-topics/83298/early-detection-and-novel-diagnostic-approaches-in-gynecological-malignancies