Gynecological Oncology: Advanced Diagnosis and Management

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 3399

Special Issue Editors


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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

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Guest Editor
Department of Obstetrics and Gynecology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
Interests: clinical oncology; gynecological oncology; gyneco-oncology surgery; HPV
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Guest Editor
Department of Obstetrics and Gynecology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
Interests: immunology of heathy and pathologic pregnancy; immunology of gynecologic malignancies; operative gynecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Gynaecological oncology is rapidly evolving with a growing demand for advancements in diagnosis and management strategies. The need for early detection, improved treatment options, and a better understanding of the underlying mechanisms of gynaecological cancers has never been more important. This Special Issue aims to bring together leading experts, researchers, and clinicians from around the world to share their insights, innovations, and research findings related to gynaecological oncology. We welcome manuscripts on the aetiology, diagnosis, and treatment of female cancers from early to advanced stages, as well as research from any discipline related to this area of ​​interest.

To ensure the highest quality of contributions, we assembled a distinguished panel of experts to serve as Guest Editors for this Special Issue. We believe that the publication of this Special Issue in Diagnostics will serve as a valuable resource for clinicians, researchers, and healthcare professionals involved in the care and management of gynaecological oncology patients. It will also contribute to the overall advancement of our understanding of these complex diseases.

Topics of interest for this Special Issue include (but are not limited to) the following:

  • New trends in gynaecologic oncology research;
  • Novel diagnostic techniques and biomarkers in gynaecological cancers;
  • Advances in surgical interventions and minimally invasive procedures;
  • Personalised treatment approaches for gynaecological malignancies;
  • Multidisciplinary care and collaborative management strategies;
  • Novelties in histopathological processing;
  • Radiological staging of gynaecologic cancer;
  • Surgical staging of gynaecologic cancer;
  • Pre- and postoperative care;
  • Standardization of surgical methods;
  • Sentinel lymph node mapping;
  • Role of image-guided radiation therapy;
  • Neoadjuvant and adjuvant chemotherapy;
  • Palliative care and quality-of-life considerations;
  • Artificial intelligence, machine learning, emerging digital technologies, and their impact in gynaecological oncology.

Dr. Alexandros Laios
Dr. Zoard T. Krasznai
Dr. Rudolf Lampé
Guest Editors

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. Diagnostics 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

  • gynecology
  • oncology
  • endometrial cancer
  • ovarian cancer
  • cervical cancer
  • diagnosis
  • staging
  • surgical methods
  • standardization of surgery
  • treatment
  • innovation
  • precision medicine
  • artificial intelligence
  • machine learning

Published Papers (2 papers)

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Research

14 pages, 878 KiB  
Article
Visualization Methods for Uterine Sentinel Lymph Nodes in Early-Stage Endometrial Carcinoma: A Comparative Analysis
by Linas Andreika, Karolina Vankevičienė, Diana Ramašauskaitė and Vilius Rudaitis
Diagnostics 2024, 14(5), 552; https://doi.org/10.3390/diagnostics14050552 - 5 Mar 2024
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Abstract
Background: Sentinel lymph node (SLN) biopsy in early-stage endometrial cancer is recommended over systematic lymphadenectomy due to reduced morbidity and comparable detection rates. The main objective of this study was to compare the overall and bilateral detection rates of SLN in early-stage endometrial [...] Read more.
Background: Sentinel lymph node (SLN) biopsy in early-stage endometrial cancer is recommended over systematic lymphadenectomy due to reduced morbidity and comparable detection rates. The main objective of this study was to compare the overall and bilateral detection rates of SLN in early-stage endometrial cancer using three techniques. Methods: a prospective cohort study was designed to detect the difference in SLN detection rate in three cohorts: Indocyanine green (ICG), methylene blue (MB), and tracer combination (ICG + MB). Mapping characteristics, detection rate, number of SLNs, and positive SLNs of the three cohorts were compared. Results: A total of 99 patients were enrolled. A total of 109 SLN sites with 164 lymph nodes were detected. No differences were found between the three cohorts in terms of age, BMI, tumor diameter, or other histologic characteristics. The overall SLN detection rate (DR) was 54.3% in the MB group, 72.7% in ICG, and 80.6% in the ICG-MB group. Bilateral DR was 22.9%, 39.4%, and 54.8% in groups, respectively, with the MB method yielding significantly inferior results. Conclusions: The ICG-MB group demonstrated superior overall and bilateral detection rates, but a significant difference was found only in the MB cohort. Combining tracer agents can enhance the accuracy of SLN identification in initial-stage endometrial cancer without additional risk to the patient. Full article
(This article belongs to the Special Issue Gynecological Oncology: Advanced Diagnosis and Management)
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13 pages, 1065 KiB  
Article
Explaining the Elusive Nature of a Well-Defined Threshold for Blood Transfusion in Advanced Epithelial Ovarian Cancer Cytoreductive Surgery
by Alexandros Laios, Evangelos Kalampokis, Marios-Evangelos Mamalis, Amudha Thangavelu, Yong Sheng Tan, Richard Hutson, Sarika Munot, Tim Broadhead, David Nugent, Georgios Theophilou, Robert-Edward Jackson and Diederick De Jong
Diagnostics 2024, 14(1), 94; https://doi.org/10.3390/diagnostics14010094 - 30 Dec 2023
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Abstract
There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. To address this, we devised a Machine Learning (ML)-driven prediction algorithm aimed at prompting and elucidating a communication alert for BT based on anticipated peri-operative events [...] Read more.
There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. To address this, we devised a Machine Learning (ML)-driven prediction algorithm aimed at prompting and elucidating a communication alert for BT based on anticipated peri-operative events independent of existing BT policies. We analyzed data from 403 EOC patients who underwent cytoreductive surgery between 2014 and 2019. The estimated blood volume (EBV), calculated using the formula EBV = weight × 80, served for setting a 10% EBV threshold for individual intervention. Based on known estimated blood loss (EBL), we identified two distinct groups. The Receiver operating characteristic (ROC) curves revealed satisfactory results for predicting events above the established threshold (AUC 0.823, 95% CI 0.76–0.88). Operative time (OT) was the most significant factor influencing predictions. Intra-operative blood loss exceeding 10% EBV was associated with OT > 250 min, primary surgery, serous histology, performance status 0, R2 resection and surgical complexity score > 4. Certain sub-procedures including large bowel resection, stoma formation, ileocecal resection/right hemicolectomy, mesenteric resection, bladder and upper abdominal peritonectomy demonstrated clear associations with an elevated interventional risk. Our findings emphasize the importance of obtaining a rough estimate of OT in advance for precise prediction of blood requirements. Full article
(This article belongs to the Special Issue Gynecological Oncology: Advanced Diagnosis and Management)
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