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Diagnostics
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Diagnosis and Management of Gynecological Cancers: Third Edition
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Diagnosis and Management of Gynecological Cancers: Third Edition

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

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 10915

Special Issue Editor

Prof. Dr. Robert Jach

E-Mail Website
Guest Editor
Division of Gynecologic Endocrinology, Jagiellonian University Medical College, Kopernika 23, 31-501 Krakow, Poland
Interests: gynecologic oncology; cervical cancer; cervical cancer screening; hysterectomy; laparoscopic surgery; cancer prevention; ovary; oncology; cervical cancer prevention
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I cordially invite you to share your discoveries and observations in the field of oncological gynecology with the scientific community and the medical world. This Special Issue will publish reviews and original papers concerning recent advances in diagnostic (based on AI, radiotracers, SLN mapping, biomarkers, DNA/mRNA agents, and molecular biology), imaging (expert ultrasonography and hysteroscopy), and treatment modalities (tips and tricks in surgery, minimally invasive techniques, tailored systemic therapy, immunotherapy, side-effect management, complication management, terminal phase of cancer management, oncofertility, and cancer treatment during pregnancy) for these types of cancer.

I invite you to contribute to this fascinating field of research.

Prof. Dr. Robert Jach
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. 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

  • diagnostic technologies
  • gynecological cancers
  • ovarian cancer
  • endometrial cancer
  • diagnostic pathology
  • diagnostic image
  • machine learning

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Published Papers (6 papers)

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Research

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28 pages, 16728 KB  
Article
Deep Learning-Based DNA Methylation Detection in Cervical Cancer Using the One-Hot Character Representation Technique
by Apoorva, Vikas Handa, Shalini Batra and Vinay Arora
Diagnostics 2025, 15(17), 2263; https://doi.org/10.3390/diagnostics15172263 - 7 Sep 2025
Viewed by 399
Abstract
Background: Cervical cancer is among the most prevalent malignancies in women worldwide, and early detection of epigenetic alterations such as Deoxyribose Nucleic Acid (DNA) methylation is of utmost significance for improving clinical results. This study introduces a novel deep learning-based framework for [...] Read more.
Background: Cervical cancer is among the most prevalent malignancies in women worldwide, and early detection of epigenetic alterations such as Deoxyribose Nucleic Acid (DNA) methylation is of utmost significance for improving clinical results. This study introduces a novel deep learning-based framework for predicting DNA methylation in cervical cancer, utilizing a UNet architecture integrated with an innovative one-hot character encoding technique. Methods: Two encoding strategies, monomer and dimer, were systematically evaluated for their ability to capture discriminative features from DNA sequences. Experiments were conducted on Cytosine–Guanine (CG) sites using varying sequence window sizes of 100 bp, 200 bp, and 300 bp, and sample sizes of 5000, 10,000, and 20,000. Model validation was performed on promoter regions of five cervical cancer-associated genes: miR-100, miR-138, miR-484, hTERT, and ERVH48-1. Results: The dimer encoding strategy, combined with a 300-base pair window and 5000 CG sites, emerged as the optimal configuration. The proposed framework demonstrated better predictive performance, with an accuracy of 91.60%, sensitivity of 96.71%, specificity of 87.32%, and an Area Under the Receiver Operating Characteristic (AUROC) score of 96.53, significantly outperforming benchmark deep learning models, including Convolutional Neural Networks and MobileNet. Validation on promoter regions further confirmed the robustness of the model, as it accurately identified 86.27% of methylated CG sites and maintained a strong AUROC of 83.99, demonstrating its precision–recall balance and practical relevance during validation in promoter-region genes. Conclusions: These findings establish the potential of the proposed UNet-based approach as a reliable and scalable tool for early detection of epigenetic modifications. Thus, the work contributes significantly to improving biomarker discovery and diagnostics in cervical cancer research. Full article
(This article belongs to the Special Issue Diagnosis and Management of Gynecological Cancers: Third Edition)
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16 pages, 1973 KB  
Article
Clinical and Virological Profiles Associated with CINTEC® PLUS Positivity: A Data-Driven Clustering and Modeling Study
by Iulian-Valentin Munteanu, Demetra Socolov, Razvan Socolov, Ana-Maria Adam, Gigi Adam, Ingrid-Andrada Vasilache, Petronela Vicoveanu, Valeriu Harabor, Anamaria Harabor and Alina-Mihaela Calin
Diagnostics 2025, 15(17), 2200; https://doi.org/10.3390/diagnostics15172200 - 29 Aug 2025
Viewed by 427
Abstract
Background/Objectives: The diagnostic performance of CINtec® PLUS can be influenced by numerous patient characteristics and risk factors. The aim of this retrospective study was to evaluate and model the risk factors associated with CINtec® PLUS test positivity in patients undergoing [...] Read more.
Background/Objectives: The diagnostic performance of CINtec® PLUS can be influenced by numerous patient characteristics and risk factors. The aim of this retrospective study was to evaluate and model the risk factors associated with CINtec® PLUS test positivity in patients undergoing cervical cancer screening and to assess their predictive performance for the prediction of cervical intraepithelial neoplasia (CIN) 2/3 using an unsupervised machine learning-based model. Methods: Medical data of 134 patients with human papillomavirus (HPV) infection who underwent CINtec® PLUS testing were used to model the impact of risk factors on dual-stain cytology positivity and to evaluate the predictive performance for CIN2/3. Results: The gradient boosting classifier for the prediction of CINtec® PLUS positivity using clinical risk factors had a precision of 75%, an overall accuracy of 0.62, and an area under the curve (AUC) value of 0.77. Body mass index and age were the most important variables in this model. HSIL, ASC-US, and other high-risk HPV strains increased the likelihood of a positive outcome. Overall AUC values for a positive test alone were 0.74 and 0.69 for CIN2 and CIN3 prediction, respectively. For CIN2 prediction, the XGBoost model performed well, with 71% sensitivity, 85% specificity, and an AUC value of 0.90. However, the model had 96% sensitivity, 25% specificity, and 0.58 AUC for CIN3 prediction. Conclusions: Patient characteristics and risk factors can influence CINtec® PLUS positivity rates and they need to be carefully considered before choosing a specific management. Full article
(This article belongs to the Special Issue Diagnosis and Management of Gynecological Cancers: Third Edition)
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18 pages, 1473 KB  
Article
Neutrophil to Lymphocyte Ratio a Prognostic Tool in Endometrial Cancer Among Classical Prognostic Factors
by Alexandra Timea Kirsch-Mangu, Alexandru Țîpcu, Vlad Alexandru Gâta, Diana Cristina Pop, Zsolt Fekete, Alexandru Irimie and Paul Milan Kubelac
Diagnostics 2025, 15(17), 2172; https://doi.org/10.3390/diagnostics15172172 - 27 Aug 2025
Viewed by 568
Abstract
Background: Endometrial cancer (EC) is the most common gynecologic malignancy in developed countries. Despite advances in diagnosis and treatment, recurrence and mortality remain significant concerns. The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has shown prognostic value in several malignancies, but its [...] Read more.
Background: Endometrial cancer (EC) is the most common gynecologic malignancy in developed countries. Despite advances in diagnosis and treatment, recurrence and mortality remain significant concerns. The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has shown prognostic value in several malignancies, but its utility in EC remains underexplored. Objective: To evaluate the prognostic significance of the preoperative NLR in patients with endometrial cancer undergoing primary surgical treatment. Methods: We conducted a retrospective cohort study including 398 patients with histologically confirmed endometrial adenocarcinoma surgically treated at a tertiary cancer center. Preoperative complete blood counts were used to calculate NLR, and a cutoff value of 2.27 was determined through Receiver Operating Characteristic (ROC) analysis. Survival outcomes were assessed using Kaplan–Meier analysis and Cox proportional hazards modeling. Results: Patients with NLR ≥ 2.27 had significantly reduced median overall survival (OS) compared to those with NLR < 2.27 (72.3 vs. 92.8 months, p = 0.008). In multivariate analysis, elevated NLR remained an independent predictor of poorer OS (HR = 1.87; 95% CI: 1.156–3.017; p = 0.011), alongside age ≥ 64 years, lymphovascular space invasion (LVSI), lymph node involvement, and distant metastases. ROC analysis yielded an Area Under the Curve (AUC) of 0.646 for NLR. Notably, vaginal brachytherapy was associated with improved survival (HR = 0.53; p = 0.026), while other adjuvant therapies were not independently significant. Conclusions: Preoperative NLR is an accessible, independent prognostic biomarker in endometrial cancer and may serve as a surrogate indicator of tumor-promoting inflammation and immune dysregulation. Its integration into preoperative assessment could enhance risk stratification and guide personalized treatment strategies. However, findings should be interpreted in light of the study’s retrospective design, single-center setting, and lack of molecular classification data. Prospective validation is warranted to confirm its clinical utility. Full article
(This article belongs to the Special Issue Diagnosis and Management of Gynecological Cancers: Third Edition)
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Review

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17 pages, 559 KB  
Review
The Application of Circulating Tumour DNA (ctDNA) in the Diagnosis, Prognosis, and Treatment Monitoring of Gynaecological and Breast Cancers (Review)
by Aleksandra Englisz, Marta Smycz-Kubańska, Patrycja Królewska-Daszczyńska, Magdalena Błaut, Agnieszka Duszyc and Aleksandra Mielczarek-Palacz
Diagnostics 2025, 15(10), 1289; https://doi.org/10.3390/diagnostics15101289 - 21 May 2025
Viewed by 1412
Abstract
Gynaecological cancers, including endometrial, ovarian, and cervical cancers as well as breast cancer, despite numerous studies, still constitute a challenge for modern oncology. For this reason, research aimed at the application of modern diagnostic methods that are useful in early detection, prognosis, and [...] Read more.
Gynaecological cancers, including endometrial, ovarian, and cervical cancers as well as breast cancer, despite numerous studies, still constitute a challenge for modern oncology. For this reason, research aimed at the application of modern diagnostic methods that are useful in early detection, prognosis, and treatment monitoring deserves special attention, Great hopes are currently being placed on the use of liquid biopsy (LB), which examines various tumour components, including cell-free RNA (cfRNA), circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), exosomes, and tumour-educated platelets (TEPs). LB has shown promise as a minimally invasive means of early diagnosis of cancers, detection of recurrence, prediction of therapy response, treatment monitoring, and drug selection. The integration of this test into clinical practice in modern oncology is challenging, but offers many benefits, including reducing the risks associated with invasive procedures, improving diagnostic and therapeutic efficacy, and improving the quality of life of oncology patients. The aim of this review is to present recent reports on the use of ctDNA in diagnosing, predicting the outcome of, and monitoring the treatment of gynaecological and breast cancers. Full article
(This article belongs to the Special Issue Diagnosis and Management of Gynecological Cancers: Third Edition)
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17 pages, 1024 KB  
Review
Sensitivity and Specificity of Selected Biomarkers and Their Combinations in the Diagnosis of Ovarian Cancer
by Aleksandra Englisz, Marta Smycz-Kubańska and Aleksandra Mielczarek-Palacz
Diagnostics 2024, 14(9), 949; https://doi.org/10.3390/diagnostics14090949 - 30 Apr 2024
Cited by 11 | Viewed by 6708
Abstract
One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and [...] Read more.
One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers’ efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage. Full article
(This article belongs to the Special Issue Diagnosis and Management of Gynecological Cancers: Third Edition)
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Other

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10 pages, 1959 KB  
Case Report
Rectal Clear Cell Carcinoma Arising from Endometriosis: Case Report and Literature Review
by Adriana Ioana Gaia-Oltean, Dan Boitor-Borza, Voicu Caius Simedrea, Vlad Braicu, Laura-Ancuta Pop and Romeo Micu
Diagnostics 2025, 15(15), 1936; https://doi.org/10.3390/diagnostics15151936 - 31 Jul 2025
Viewed by 629
Abstract
Background and Clinical Significance: Endometriosis is a common gynecological disease that can occasionally be associated with malignant transformation. The most common site of malignant transformation is the ovary, but there can also be rare extragonadal endometriosis-associated malignancy sites, such as the intestines, rectovaginal [...] Read more.
Background and Clinical Significance: Endometriosis is a common gynecological disease that can occasionally be associated with malignant transformation. The most common site of malignant transformation is the ovary, but there can also be rare extragonadal endometriosis-associated malignancy sites, such as the intestines, rectovaginal septum, and abdominal wall. A low number of malignant degenerations of rectal endometriosis are described in the literature. However, the majority of these cases report endometrioid adenocarcinoma as the most frequent histopathological type of tumor. On the other hand, Müllerian clear cell carcinoma is sporadic. Case Presentation: We present the case of a 43-year-old woman with clear cell carcinoma of the rectum, which developed on an endometriosis nodule, and the surgical outcome. Imaging of the case was performed by MRI. The patient was offered curative surgery. The pathology report confirmed a clear cell carcinoma developed on an endometriosis lesion, and immunochemistry helped in the characterization of the tumor. The patient developed a rectovaginal fistula. An ileostomy and surgical repair of the fistulous opening were performed, with a favorable postoperative recovery. Conclusions: Malignant transformation of endometriosis lesions is possible and should be taken into consideration. Müllerian clear cell carcinoma development within rectovaginal endometriosis is extremely rare. Full article
(This article belongs to the Special Issue Diagnosis and Management of Gynecological Cancers: Third Edition)
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