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Cancers
Special Issues
Radiomics in Gynaecological Cancers
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Radiomics in Gynaecological Cancers

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 1 July 2025 | Viewed by 2039

Special Issue Editors

Dr. Priya Bhosale

E-Mail Website
Guest Editor
Department of Abdominal Imaging, Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: PET/MR; imaging biomarkers; MR and CT in gynecologic and pancreatic malignancies
Special Issues, Collections and Topics in MDPI journals
Dr. Mayur Virarkar

E-Mail Website
Guest Editor
Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
Interests: fluorodeoxyglucose F 18; positron emission tomography computed tomography; uterine cervical neoplasms

Special Issue Information

Dear Colleagues,

Cancers welcomes contributions from researchers, clinicians, and industry professionals dedicated to advancing radiomics in gynecologic oncology. It aims to bridge the gap between research and clinical practice.

We are pleased to invite you to submit your manuscript to this Special Issue entitled ‘Radiomics in Gynecological Cancers’.

This Special Issue aims to advance the knowledge of radiomics for gynecologic malignancies by providing a platform for disseminating high-quality research, reviews, and case studies. Cancers seeks to enhance the understanding of how radiomic techniques can be applied to improve the diagnosis, prognosis, and treatment of gynecologic cancers, which will contribute to better patient outcomes and personalized medicine.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but

not limited to) the following:

  1. Studies on the development and validation of novel radiomic features derived from medical imaging modalities such as MRI, CT, and PET specific to gynecologic malignancies.
  2. Research focused on applying radiomics in clinical settings, including early detection, treatment planning, and response monitoring in ovarian, cervical, endometrial, and other gynecologic cancers.
  3. Investigations exploring the correlation between radiomic features and clinical outcomes, genetic profiles, and molecular markers to provide deeper insights into tumor biology and potential therapeutic targets.
  4. Articles on technological advancements in imaging techniques, machine learning algorithms, and computational methods that enhance the extraction and analysis of radiomic data.
  5. Encouragement of interdisciplinary research combining radiomics with other fields such as pathology, oncology, and genomics to foster a comprehensive approach to gynecologic cancer research.
  6. Comprehensive reviews summarizing the current state of radiomics in gynecologic oncology and case studies illustrating the practical applications and benefits of radiomic analysis in real-world clinical scenarios.
  7. Discussions on the ethical, regulatory, and practical challenges in implementing radiomic techniques in routine clinical practice, ensuring patient safety, and maintaining data integrity.

Dr. Priya Bhosale
Dr. Mayur Virarkar
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 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. Cancers 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 2900 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

  • MRI
  • CT
  • PET
  • radiomics
  • gynecologic malignancies

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

Published Papers (2 papers)

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Research

13 pages, 6113 KiB  
Article
Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging in Magnetic Resonance in the Assessment of Peritoneal Recurrence of Ovarian Cancer in Patients with or Without BRCA Mutation
by Melania Jankowska-Lombarska, Laretta Grabowska-Derlatka, Leszek Kraj and Pawel Derlatka
Cancers 2024, 16(22), 3738; https://doi.org/10.3390/cancers16223738 - 5 Nov 2024
Viewed by 856
Abstract
Background: The aim of this study was to determine the differences in diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) parameters between patients with peritoneal high-grade serous ovarian cancer (HGSOC) recurrence with BRCA mutations (BRCAmut) or BRCA wild type (BRCAwt). Materials and Methods: [...] Read more.
Background: The aim of this study was to determine the differences in diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) parameters between patients with peritoneal high-grade serous ovarian cancer (HGSOC) recurrence with BRCA mutations (BRCAmut) or BRCA wild type (BRCAwt). Materials and Methods: We retrospectively analyzed the abdominal and pelvic magnetic resonance (MR) images of 43 patients suspected of having recurrent HGSOC, of whom 18 had BRCA1/2 gene mutations. Patients underwent MRI examination via a 1.5 T MRI scanner, and the analyzed parameters were as follows: apparent diffusion coefficient (ADC), time to peak (TTP) and perfusion maximum enhancement (Perf. Max. En.). Results: The mean ADC in patients with BRCAwt was lower than that in patients with BRCAmut: 788.7 (SD: 139.5) vs. 977.3 (SD: 103), p-value = 0.00002. The average TTP value for patients with BRCAwt was greater than that for patients with mutations: 256.3 (SD: 50) vs. 160.6 (SD: 35.5), p-value < 0.01. The Perf. Max. En. value was lower in the BRCAwt group: 148.6 (SD: 12.3) vs. 233.6 (SD: 29.2), p-value < 0.01. Conclusion: Our study revealed a statistically significant correlation between DWI and DCE parameters in examinations of peritoneal metastasis in patients with BRCA1/2 mutations. Adding DCE perfusion to the MRI protocol for ovarian cancer recurrence in patients with BRCAmut may be a valuable tool. Full article
(This article belongs to the Special Issue Radiomics in Gynaecological Cancers)
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11 pages, 694 KiB  
Article
MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma
by Mayur Virarkar, Taher Daoud, Jia Sun, Matthew Montanarella, Manuel Menendez-Santos, Hagar Mahmoud, Mohammed Saleh and Priya Bhosale
Cancers 2024, 16(15), 2647; https://doi.org/10.3390/cancers16152647 - 25 Jul 2024
Viewed by 899
Abstract
The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) [...] Read more.
The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher’s exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the “256 Angular Variance of Energy” texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes. Full article
(This article belongs to the Special Issue Radiomics in Gynaecological Cancers)
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