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editorial
. 2025 Aug 7;15(15):1976. doi: 10.3390/diagnostics15151976

Exploring Gynecological Pathology and Imaging: A Crossroads of Technology, Biology, and Care

Graziella Di Grezia 1
PMCID: PMC12346558  PMID: 40804940

The field of gynecologic diagnostics is undergoing a profound transformation. No longer confined to traditional imaging and histopathology, it is now being reimagined through the integration of advanced technologies, interdisciplinary collaboration, and an increasingly patient-centered clinical lens.

This Special Issue of Diagnostics, “Exploring Gynecological Pathology and Imaging,” gathers nine contributions that reflect this evolution. The published articles span breast imaging physics [contributions 1, 2], cervical cancer prevention strategies [contribution 3], fibroid diagnosis pathways [contribution 4], applications of artificial intelligence in urogynecology [contribution 5], standardized approaches to vulvar ultrasound [contribution 6], already cervical cancer prevention strategies [contributions 7, 8], and even the prognostic potential of PET/CT parameters in ovarian cancer [contribution 9]. Together, these works show that innovation in gynecological imaging and pathology is no longer optional—it is foundational.

Some contributions offer fresh clinical perspectives on long-debated challenges. For example, the study by Cantatore et al. [contribution 8] proposes a novel risk stratification model for recurrence in patients treated for high-grade cervical intraepithelial neoplasia. This model integrates HPV persistence, margin status, and inflammatory markers—moving decisively beyond single-parameter evaluations. Similarly, Tsampazis et al. [contribution 3] assess the diagnostic accuracy of HPV mRNA testing and immunohistochemical markers (p16/Ki67), suggesting meaningful refinements in colposcopic triage.

Technological and methodological innovation are also evident in our own contributions to this issue [contributions 1, 2], in which we explored the foundational physics of mammography, tomosynthesis, and contrast-enhanced imaging. Our aim was to make complex technical knowledge clinically accessible, thereby supporting more nuanced decision-making in breast diagnostics.

Beyond image acquisition and interpretation, this Special Issue highlights how AI [contribution 5], self-sampling technologies [contribution 7], and hybrid diagnostic algorithms can amplify access and precision. In doing so, it echoes the growing global consensus that gynecologic care must become more inclusive, predictive, and adaptive.

From a personal perspective, the themes of this Special Issue are closely aligned with my own research over the past decade, which has addressed the clinical relevance of radiologic tools, cost-effective imaging strategies, and psychological–phenotypic correlates in breast disease. I am particularly interested in how hybrid human–machine systems—grounded in radiomics and intelligent imaging—can transform diagnosis into a dynamic process of care rather than a static endpoint. Radiomics, in particular, bridges the gap between medical images and personalized medicine by extracting quantitative data that inform clinical decisions [1,2].

This collection of articles is not the end of a journey, but a new point of departure. My deepest thanks go to all the contributing authors, the reviewers for their generous insight, and the editorial staff at Diagnostics for their professional support. Moreover, addressing potential biases in artificial intelligence systems is crucial to ensure equitable and effective healthcare delivery [3].

We hope this Special Issue will serve as an open window on the future of women’s health diagnostics: a space where biology, data, and clinical wisdom converge. The advances in deep learning and hybrid imaging approaches promise to enhance screening accuracy and clinical outcomes, especially in breast cancer detection and management [4,5].

Conflicts of Interest

The author declares no conflicts of interest.

List of Contributions

  1. Fico, N.; Di Grezia, G.; Cuccurullo, V.; Salvia, A.A.H.; Iacomino, A.; Sciarra, A.; Gatta, G. Breast Imaging Physics in Mammography (Part I). Diagnostics 2023, 13, 3227. https://doi.org/10.3390/diagnostics13203227.

  2. Fico, N.; Di Grezia, G.; Cuccurullo, V.; Salvia, A.A.H.; Iacomino, A.; Sciarra, A.; La Forgia, D.; Gatta, G. Breast Imaging Physics in Mammography (Part II). Diagnostics 2023, 13, 3582. https://doi.org/10.3390/diagnostics13233582.

  3. Tsampazis, N.; Vavoulidis, E.; Margioula-Siarkou, C.; Symeonidou, M.; Intzes, S.; Papanikolaou, A.; Dinas, K.; Daniilidis, A. The Diagnostic Accuracy of Electrical Impedance Spectroscopy-Assisted Colposcopy, HPV mRNA Test, and P16/Ki67 Immunostaining as CIN2+ Predictors in Greek Population. Diagnostics 2024, 14, 1379. https://doi.org/10.3390/diagnostics14131379.

  4. Centini, G.; Cannoni, A.; Ginetti, A.; Colombi, I.; Giorgi, M.; Schettini, G.; Martire, F.G.; Lazzeri, L.; Zupi, E. Tailoring the Diagnostic Pathway for Medical and Surgical Treatment of Uterine Fibroids: A Narrative Review. Diagnostics 2024, 14, 2046. https://doi.org/10.3390/diagnostics14182046.

  5. Oliveira, M.B.M.d.; Mendes, F.; Martins, M.; Cardoso, P.; Fonseca, J.; Mascarenhas, T.; Saraiva, M.M. The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects. Diagnostics 2025, 15, 274. https://doi.org/10.3390/diagnostics15030274.

  6. Montik, N.; Grelloni, C.; Delli Carpini, G.; Petrucci, J.; Di Giuseppe, J.; Ciavattini, A. Transperineal Vulvar Ultrasound: A Review of Normal and Abnormal Findings with a Proposed Standardized Methodology. Diagnostics 2025, 15, 627. https://doi.org/10.3390/diagnostics15050627.

  7. Gomes, M.; Provaggi, E.; Pembe, A.B.; Olaitan, A.; Gentry-Maharaj, A. Advancing Cervical Cancer Prevention Equity: Innovations in Self-Sampling and Digital Health Technologies Across Healthcare Settings. Diagnostics 2025, 15, 1176. https://doi.org/10.3390/diagnostics15091176.

  8. Cantatore, F.; Agrillo, N.; Camussi, A.; Colella, L.; Origoni, M. Proposal of a Risk Stratification Model for Recurrence After Excisional Treatment of High-Grade Cervical Intraepithelial Neoplasia (HG-CIN). Diagnostics 2025, 15, 1585. https://doi.org/10.3390/diagnostics15131585.

  9. Glickman, A.; Gil-Ibáñez, B.; Niñerola-Baizán, A.; Tormo, M.; Carreras-Dieguez, N.; Fusté, P.; Del Pino, M.; González-Bosquet, E.; Romero-Zayas, I.; Celada-Castro, C.; et al. PET/CT Volumetric Parameters as Predictors of the Peritoneal Cancer Index in Advanced Ovarian Cancer Patients. Diagnostics 2025, 15, 1818. https://doi.org/10.3390/diagnostics15141818.

Funding Statement

This research received no external funding.

Footnotes

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References

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Articles from Diagnostics are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

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