Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review
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Le résumé fourni par la source
Background/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review
- Date Crossref
- 22/10/2025
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Pirogov Russian National Research Medical University Pathology and Clinical Pathology Department pays non établi dans la noticeUniversité ou école supérieure
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National Medical Research Center for Obstetrics pays non établi dans la noticeStructure de recherche
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Universidade do Porto pays non établi dans la noticeUniversité ou école supérieure
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Fernando Pessoa University pays non établi dans la noticeUniversité ou école supérieure
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Institute for System Programming pays non établi dans la noticeStructure de recherche
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Hospital Universitari Arnau de Vilanova pays non établi dans la noticeÉtablissement de santé
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FSBI “National Medical Research Centre for Obstetrics pays non établi dans la noticeStructure de recherche
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FSBI "National Medical Research Centre for Obstetrics pays non établi dans la noticeStructure de recherche
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Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP) Pathology Laboratory pays non établi dans la noticeUniversité ou école supérieure
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Universidade Fernando Pessoa Escola de Medicina e Ciências Biomédicas pays non établi dans la noticeUniversité ou école supérieure
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Hospital U Arnau de Vilanova & University of Lleida Department of Pathology pays non établi dans la noticeUniversité ou école supérieure
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Medical Faculty of University of Porto Pathology Department pays non établi dans la noticeUniversité ou école supérieure
Pathology and Clinical Pathology Department — Pirogov Russian National Research Medical University, National Medical Research Center for Obstetrics et Universidade do Porto, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.