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Advancements in Ovarian Cancer Management: From Early Detection to Precision...
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Advancements in Ovarian Cancer Management: From Early Detection to Precision Therapies

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 415

Special Issue Editor

Dr. Alexandros Laios

E-Mail Website
Guest Editor
Department of Gynaecologic Oncology, ESGO Center of Excellence for Ovarian Cancer Surgery, St James’s University Hospital, Leeds, UK
Interests: gynaecologic oncology; artificial intelligence; machine learning; precision medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Epithelial ovarian cancer is the leading cause of death from malignancies of the female genital tract. Modern combination treatment has only slightly improved outcomes due to disease heterogeneity, drug resistance, and refractory disease in recurrent patients. Advancements in technology, a deeper understanding of tumour biology, and a focus on personalized medicine are poised to significantly influence the surgical management of advanced-stage ovarian cancer. To address this, we invite reviews and original research articles that critically evaluate the role of early detection and diagnosis; targeted, personalized, and combination therapies; improving surgical strategies; patient optimization; rare cancers; survivorship; quality of life; innovative clinical trials; and global health equity.

Submissions highlighting high-quality, original studies are particularly encouraged, especially those exploring innovative precision approaches, and the integration of artificial intelligence to maximize surgical and treatment outcomes. We hope this Special Issue will represent a comprehensive approach to ovarian cancer and align with current research priorities and clinical needs while emphasizing innovation and patient-focused care.

Possible topics for this Special Issue can include the following:

  1. The role of artificial intelligence and big data in personalized ovarian cancer care: predictive modelling and surgical planning;
  2. Enhanced precision in cytoreductive surgery: from image-guided surgery to molecular profiling;
  3. Minimally invasive surgical techniques;
  4. Optimizing timing of surgery;
  5. Biomarker discovery for early detection and prognosis in ovarian cancer;
  6. Targeting DNA repair deficiencies: the role of PARP inhibitors and beyond;
  7. Overcoming chemoresistance in ovarian cancer: mechanisms and strategies;
  8. Advances in immunotherapy for ovarian cancer: opportunities and challenges;
  9. HIPEC and beyond: optimization of surgical and perioperative treatments and standardization of protocols;
  10. Insights into rare ovarian cancer subtypes;
  11. Survivorship and quality of life in ovarian cancer patients;
  12. Global perspectives on ovarian cancer: addressing disparities in diagnosis and care;
  13. Innovative clinical trial designs for precision medicine in ovarian cancer.

The submitted manuscripts for this Special Issue will be peer-reviewed before publication.

Dr. Alexandros Laios
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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

11 pages, 2665 KiB  
Article
Mapping the Advanced-Stage Epithelial Ovarian Cancer Landscape Goes Beyond Words: Two Large Language Models, Eight Tasks, One Journey
by Michela Quaranta, Alexandros Laios, Charlie Rogers, Anastasia Ioanna Mavromatidou, Amudha Thangavelu, Georgios Theophilou, David Nugent, Diederick DeJong and Evangelos Kalampokis
J. Clin. Med. 2025, 14(7), 2223; https://doi.org/10.3390/jcm14072223 - 25 Mar 2025
Viewed by 228
Abstract
Background/Objectives: The advancement of natural language processing (NLP) technologies has transformed various sectors. However, their application in the healthcare domain, particularly for analysing clinical notes, remains underdeveloped. We investigated the use of deep neural networks, specifically transformer-based models, to predict intraoperative and post-operative [...] Read more.
Background/Objectives: The advancement of natural language processing (NLP) technologies has transformed various sectors. However, their application in the healthcare domain, particularly for analysing clinical notes, remains underdeveloped. We investigated the use of deep neural networks, specifically transformer-based models, to predict intraoperative and post-operative outcomes related to advanced-stage epithelial ovarian cancer cytoreduction (aEOC) using unstructured surgical notes. Methods: We evaluated the performance of RoBERTa, a general-purpose language model, and GatorTron, a domain-specific model, across eight binary classification tasks using the same dataset. The dataset consisted of 560 surgical records from patients with aEOC who underwent cytoreductive surgery at a tertiary UK reference centre. Predictive outcomes were converted into binary features to facilitate classification tasks. To enhance the contextual information available to the models, textual data from “operative findings” and “operative notes” were concatenated. Results: Our findings highlight the tangible benefits of employing domain-specific language models for clinical text analysis. GatorTron generally outperformed RoBERTa across most predictive tasks, underscoring the advantages of domain-specific pretraining for understanding medical terminology and context. Both models struggled to predict certain outcomes, particularly those involving post-operative events like major complications and length of hospital stay, despite adjustments in hyperparameters and training strategies. This limitation suggests that operative text alone may not sufficiently capture the complexities of post-operative recovery. Conclusions: These findings have valuable implications for developing medical AI systems to improve the delivery of modern aEOC healthcare. Full article
(This article belongs to the Special Issue Advancements in Ovarian Cancer Management: From Early Detection to Precision Therapies)
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