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Medicina
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Gynecological Surgery: Bridging Research and Clinical Practice
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Gynecological Surgery: Bridging Research and Clinical Practice

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Obstetrics and Gynecology".

Deadline for manuscript submissions: 28 August 2026 | Viewed by 2388

Special Issue Editor

Dr. Vito Andrea Capozzi

E-Mail Website
Guest Editor
Department of Medicine and Surgery, University Hospital of Parma, 43125 Parma, Italy
Interests: gynecologic oncology; endometrial cancer; ovarian cancer; cervical cancer; surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Minimally invasive gynecologic surgery has transformed pelvic floor reconstruction over the past three decades. Since the introduction of laparoscopic and robotic sacrocolpopexy, surgical management of pelvic organ prolapse has shifted toward techniques that optimize anatomical restoration while reducing morbidity, hospital stay, and recovery time. Advances in mesh technology, nerve-sparing approaches, and refined anatomical understanding have further improved functional outcomes and safety profiles. In parallel, minimally invasive gynecologic oncology has rapidly evolved, enabling precise staging, radical hysterectomy, and lymphadenectomy with reduced complications and faster recovery compared with open surgery. Innovations in imaging, sentinel-node mapping, and robotic platforms continue to redefine standards across benign and oncologic fields, emphasizing individualized, high-value, and patient-centered care.

This Special Issue will provide a comprehensive platform that integrates basic, translational, and clinical research in gynecological surgery. Our goal is to highlight innovative approaches, emerging technologies, and multimodal strategies with the potential to shape future standards of care. By fostering dialogue among researchers, clinicians, and healthcare professionals, we seek to accelerate the translation of scientific knowledge into clinical decision-making and patient management.

We welcome contributions that explore recent advances in genomics, proteomics, tumor microenvironment, immunology, and biomarker discovery, as well as developments in imaging, minimally invasive surgery, and enhanced recovery pathways. Studies investigating novel systemic treatments, including targeted therapies, immunomodulatory strategies, and combination approaches, are particularly encouraged. Additionally, submissions addressing health disparities, survivorship, real-world evidence, digital health tools, and AI-driven predictive models represent an essential component of this Special Issue.

This Special Issue invites high-quality original research articles, systematic reviews, and meta-analyses. Submissions should focus on innovative findings with clear implications for the diagnosis, prognosis, prevention, or treatment of gynecological cancers. Interdisciplinary and collaborative studies that bridge laboratory discoveries with clinical application are especially welcome.

Dr. Vito Andrea Capozzi
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 250 words) can be sent to the Editorial Office for assessment.

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. Medicina is an international peer-reviewed open access monthly 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 2200 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 surgery
  • gynecology oncology
  • translational research
  • urogynecology
  • endometrial cancer
  • ovarian cancer
  • biomarkers

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Published Papers (3 papers)

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Research

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13 pages, 1640 KB  
Article
An AI-Driven Clinical Decision Support Model Based on Anemia and Fibroid Parameters to Guide Surgical Decision-Making
by İnci Öz, Ecem Esma Yegin, Ali Utku Öz and Engin Ulukaya
Medicina 2026, 62(3), 555; https://doi.org/10.3390/medicina62030555 - 17 Mar 2026
Viewed by 450
Abstract
Background and Objectives: This study aimed to identify the clinical factors associated with the need for surgical intervention in women with uterine fibroids (UFs) and develop a data-driven clinical decision helper algorithm. By comparing hematologic and fibroid characteristics and prospectively assessing clinical [...] Read more.
Background and Objectives: This study aimed to identify the clinical factors associated with the need for surgical intervention in women with uterine fibroids (UFs) and develop a data-driven clinical decision helper algorithm. By comparing hematologic and fibroid characteristics and prospectively assessing clinical concordance with the model predictions, we sought to create an objective tool for surgical decision-making. Materials and Methods: This retrospective study enrolled 618 women with UFs who were evaluated at three participating hospitals. Of these, 238 (38.5%) underwent surgery. Comparative statistical analyses were conducted between patients who underwent myomectomy and those who did not. Machine learning (ML) models were trained to predict myomectomy necessity. A clinical concordance assessment was conducted using 50 cases that were evaluated in real time by a gynecologist blinded to both the clinical outcomes and the model outputs. Agreement between clinical assessment and algorithm-based predictions was subsequently evaluated. Results: Hemoglobin and ferritin concentrations were significantly reduced in the surgery group compared with the non-surgery group (p < 0.001). ML analyses integrating fibroid characteristics with anemia-related markers identified support vector ML models as the most accurate classifiers. Ferritin-based models achieved accuracies of 98–99% and near-perfect ROC–AUC values. ML models combining UF number or volume with ferritin demonstrated the highest precision, sensitivity, and F1-scores. Clinical concordance analysis showed 98% agreement with the blinded gynecologist, with only one borderline discordant case. Conclusions: This decision helper algorithm provides a highly accurate and objective tool for predicting surgical necessity in patients with UFs. Anemia status and fibroid characteristics were the strongest predictors. By reducing subjective variability and closely reflecting expert reasoning, the model offers a practical framework for integration into routine gynecologic decision-making. Full article
(This article belongs to the Special Issue Gynecological Surgery: Bridging Research and Clinical Practice)
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18 pages, 988 KB  
Article
HbA1c as a Key Metabolic Marker in Predicting Myomectomy Requirement in Women with Uterine Fibroids: A Machine Learning Study
by Inci Öz, Ecem E. Yegin, Ali Utku Öz and Engin Ulukaya
Medicina 2026, 62(3), 500; https://doi.org/10.3390/medicina62030500 - 9 Mar 2026
Viewed by 693
Abstract
Background and Objectives: Uterine fibroids are common benign tumors that frequently require surgical management, particularly myomectomy, in women of reproductive age. Metabolic dysfunction and insulin resistance have been implicated in fibroid biology; however, the clinical relevance of glycated hemoglobin (HbA1c) in predicting [...] Read more.
Background and Objectives: Uterine fibroids are common benign tumors that frequently require surgical management, particularly myomectomy, in women of reproductive age. Metabolic dysfunction and insulin resistance have been implicated in fibroid biology; however, the clinical relevance of glycated hemoglobin (HbA1c) in predicting myomectomy requirement remains unclear. This study aimed to evaluate the predictive role of HbA1c for myomectomy requirement in women with uterine fibroids using conventional statistical analyses and machine learning-based models under real-world clinical decision-making conditions. Materials and Methods: This study evaluated data from a retrospective multicenter cohort comprising 618 women with a diagnosis of uterine fibroids. Patients were stratified according to myomectomy status (performed vs. not performed). Comparative analyses, univariate and multivariate logistic regression, and machine learning modeling were conducted using demographic, laboratory, hormonal, and fibroid-related variables. A total of 155 machine learning models were trained, and the top 20 models with the highest accuracy were evaluated. Blinded concordance analysis was conducted on 50 independent, anonymized cases evaluated by a gynecologist who was blinded to the study data. Results: Patients undergoing myomectomy (38.5%) had significantly higher HbA1c levels than non-surgical patients (5.57 ± 0.32 vs. 5.03 ± 0.61, p < 0.001). HbA1c showed a strong association with myomectomy requirement in univariate analysis (OR 0.026, 95% CI 0.012–0.055) but lost significance in multivariate models, while ferritin remained independently associated. Machine learning models incorporating HbA1c, ferritin, hormonal, and fibroid parameters achieved accuracies between 0.99 and 1.00. Blinded concordance analysis demonstrated 94% concordance between model predictions and expert clinical judgment. Conclusions: HbA1c is a valuable integrative marker in predicting myomectomy requirement when evaluated within multidimensional machine learning frameworks, although its independent effect is confounded by iron-related parameters. These findings support the use of HbA1c as part of a comprehensive decision-support approach in uterine fibroid management. Full article
(This article belongs to the Special Issue Gynecological Surgery: Bridging Research and Clinical Practice)
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Other

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15 pages, 574 KB  
Systematic Review
Proton Beam Therapy in Gynecological Cancers: A Systematic Review of Indications, Complications, and Limitations
by Vito Andrea Capozzi, Giulia Martignon, Elisa Scarpelli, Alessandra De Finis, Stefano Restaino, Giuseppe Vizzielli and Roberto Berretta
Medicina 2026, 62(2), 334; https://doi.org/10.3390/medicina62020334 - 6 Feb 2026
Cited by 1 | Viewed by 952
Abstract
Background and Objectives: Gynecological cancers frequently require radiation therapy (RT) in primary, adjuvant, or salvage settings. However, photon-based RT is associated with non-negligible toxicity, and treatment of pelvic recurrences after prior irradiation remains challenging. Proton beam therapy (PBT), due to its favorable [...] Read more.
Background and Objectives: Gynecological cancers frequently require radiation therapy (RT) in primary, adjuvant, or salvage settings. However, photon-based RT is associated with non-negligible toxicity, and treatment of pelvic recurrences after prior irradiation remains challenging. Proton beam therapy (PBT), due to its favorable dose distribution and reduced exposure of organs at risk (OARs), has emerged as a potential alternative, particularly in re-irradiation scenarios. Despite its expanding use in other malignancies, evidence supporting PBT in gynecologic cancers remains limited. This systematic review aims to investigate the use of PBT in gynecological cancers and its associated complications. Materials and Methods: This systematic review was conducted according to PRISMA guidelines and registered in PROSPERO. A comprehensive search (2000–2025) identified studies investigating PBT in gynecologic cancers. Eligible designs included randomized trials and prospective and retrospective series. Reported adverse events were categorized as GI, GU, or other, and only grade ≥3 CT-CAE complications were considered. Results: Of 580 records screened, 9 studies comprising 232 patients met inclusion criteria. Most patients were treated for endometrial (n = 147) or cervical (n = 75) cancer; 90 received chemotherapy. Overall, severe toxicity occurred in 15.2% of patients. GI complications ranged from 0–14% and GU from 0–33%. Complication rates were lowest in adjuvant or de novo treatment series (0–10%), whereas re-irradiation cohorts showed higher rates (up to 33% GU). Comparative studies suggested a possible advantage of PBT over IMRT, particularly for GI toxicity, though data remain limited. Conclusions: Severe GI and GU toxicity after PBT in gynecologic cancers appears infrequent, particularly in primary and adjuvant settings, though re-irradiation remains challenging. Current evidence is restricted to small and heterogeneous studies. Ongoing phase II trials will provide prospective data to clarify feasibility, toxicity, and long-term outcomes. Until then, PBT in gynecologic oncology should be regarded as investigational. Full article
(This article belongs to the Special Issue Gynecological Surgery: Bridging Research and Clinical Practice)
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