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Recent Issues in Gynecologic Cancer Prevention and Screening
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Recent Issues in Gynecologic Cancer Prevention and Screening

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 875

Special Issue Editor

Prof. Dr. Taek Sang Lee

E-Mail Website
Guest Editor
Seoul Metropolitan Government—Seoul National University Borame Medical Center, Seoul, Republic of Korea
Interests: gynecologic oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of gynecological cancer screening is rapidly evolving, driven by advancements in diagnostic technology, an increasing understanding of risk factors, and a global focus on improving early detection. Despite these advances, significant challenges remain, including disparities in access to screening, variability in guidelines, and the need for more precise and personalized approaches to early diagnosis.

This Special Issue, “Recent Issues in Gynecologic Cancer Prevention and Screening”, will provide a comprehensive platform for the latest research, innovative methodologies, and critical reviews in this vital area. We are particularly interested in studies that address the following:

  • Emerging technologies and biomarkers for gynecologic cancer detection;
  • Population-based screening strategies and their efficacy;
  • Challenges in implementing screening programs in low-resource settings;
  • The role of artificial intelligence and big data in improving diagnostic accuracy.

We invite researchers, clinicians, and public health experts to contribute original research articles, reviews, and case studies that explore these and related topics. Your work will contribute to advancing the dialog on how to improve outcomes for individuals at risk of gynecological cancers.

The deadline for manuscript submissions is 31 August 2025. All submissions will undergo a rigorous peer review process, and selected papers will be published in JCM, ensuring high visibility within the field.

Join us in shaping the future of gynecological cancer screening. We look forward to receiving your valuable contributions!

Prof. Dr. Taek Sang Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • gynecologic cancer
  • prevention
  • screening

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Published Papers (1 paper)

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Research

9 pages, 197 KiB  
Article
Diagnostic Accuracy of Artificial Intelligence vs. Oncologist Interpretation in Digital Cervicography for Abnormal Cervical Cytology
by Kyeong-A So, Eun-Bi Jang, Seung-Hyuk Shim, Sun-Joo Lee and Tae-Jin Kim
J. Clin. Med. 2025, 14(5), 1763; https://doi.org/10.3390/jcm14051763 - 6 Mar 2025
Viewed by 738
Abstract
Objective: We compared the diagnostic performance of artificial intelligence (AI) with that of a gynecologic oncologist during digital cervicography. Methods: Women with abnormal cytology who underwent cervicography between January 2019 and December 2023 were included. A gynecologic oncologist interpreted the digital cervicography and [...] Read more.
Objective: We compared the diagnostic performance of artificial intelligence (AI) with that of a gynecologic oncologist during digital cervicography. Methods: Women with abnormal cytology who underwent cervicography between January 2019 and December 2023 were included. A gynecologic oncologist interpreted the digital cervicography and the results were compared with those of the AI system. Diagnostic performances were assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for low-grade squamous intraepithelial lesions (LSILs) and high-grade squamous intraepithelial lesions (HSILs)/cancer. Cohen’s kappa quantified agreement. Results: This study included 449 women (mean age, 41.0 years). A Cohen’s kappa of 0.511 (p < 0.0001) indicated moderate agreement between the oncologist and AI. Among 226 cases of HSILs/cancer, the oncologist’s sensitivity was 62.8%, compared to 47.8% for AI, with similar specificity (81.2% vs. 83.5%). The oncologist’s PPV and NPV were 85.0% and 56.3%, respectively, whereas AI’s were 83.1% and 48.5%, respectively. For LSILs/HSILs/cancer (n = 283), the oncologist achieved 98.2% sensitivity and 44.7% specificity, compared to AI’s 93.3% sensitivity and 46.1% specificity. Both had a similar PPV (86.9% vs. 86.6%); however, the oncologist’s NPV (87.2%) exceeded AI’s 64.8%. Diagnostic accuracy for LSILs/HSILs/cancer was 86.9% for the oncologist and 82.3% for AI, whereas for HSILs/cancer, it was 69.6% and 61.0%, respectively. Conclusions: Moderate agreement was observed between the oncologist and AI. Although AI demonstrated similar performance in diagnosing cervical lesions, the oncologist achieved higher diagnostic accuracy. AI is a complementary tool and future research should refine AI algorithms to align with clinical performance. Full article
(This article belongs to the Special Issue Recent Issues in Gynecologic Cancer Prevention and Screening)
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