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Cancers
Special Issues
Surgical Innovations in Gynecologic Oncology: Endometrial and Ovarian Cancer
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Surgical Innovations in Gynecologic Oncology: Endometrial and Ovarian Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (29 November 2024) | Viewed by 5025

Special Issue Editors

Dr. Nicolae Bacalbașa

E-Mail Website
Guest Editor
1. Faculty of Medicine, University of Medicine and Pharmacy Carol Davila Bucharest, 050474 Bucharest, Romania 2. Department of Visceral Surgery, Center of Excellence in Translational Medicine, Fundeni Clinical Institute, 022328 Bucharest, Romania
Interests: ovarian cancer; gynecologic surgery; gynecologic oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Valentin Nicolae Varlas

E-Mail Website
Guest Editor
Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 010221 Bucharest, Romania
Interests: gynecologic oncology; gynecologic cancer surgery; minimally invasive surgery; immunology and genetics of tumors; endometriosis; oncofertility
Special Issues, Collections and Topics in MDPI journals
Dr. Irina Balescu

E-Mail Website
Guest Editor
Department of Surgery, Ponderas Academic Hospital, 021188 Bucharest, Romania
Interests: surgical oncology; hepato-bilio-pancreatic surgery; gynecologic oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Gynecological malignancies still represent significant health problems affecting women worldwide, especially when diagnosed in advanced stages of the disease. While, in endometrial cancer, alarming signs and symptoms, such as postmenopausal vaginal bleeding, might motivate a patient to seek medical help, in ovarian cancer the diagnosis usually occurs in advanced stages of the disease due to the fact that uncommon symptoms are present. In this respect, attention was focused on identifying new surgical procedures, new lines of targeted therapies, and genetic counseling in order to improve the long-term outcomes of these patients. Therefore, nowadays, the golden standard in such cases, consisting of debulking surgery to no residual disease, should be strongly reinforced by adding targeted therapies and genetic counseling. The aim of the current Special Issue is to highlight the surgical and oncological therapies in endometrial and ovarian cancer.

Dr. Nicolae Bacalbașa
Dr. Valentin Nicolae Varlas
Dr. Irina Balescu
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

  • endometrial cancer
  • ovarian cancer
  • personalized medicine
  • debulking surgery
  • molecular therapies
  • genetic counseling
  • high-grade tumors

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

Published Papers (3 papers)

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Research

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18 pages, 2343 KiB  
Article
The Influence of Inflammatory and Nutritional Status on the Long-Term Outcomes in Advanced Stage Ovarian Cancer
by Nicolae Bacalbasa, Sorin Petrea, Bogdan Gaspar, Lucian Pop, Valentin Varlas, Adrian Hasegan, Gabriel Gorecki, Cristina Martac, Marilena Stoian, Anca Zgura and Irina Balescu
Cancers 2024, 16(14), 2504; https://doi.org/10.3390/cancers16142504 - 10 Jul 2024
Cited by 1 | Viewed by 1156
Abstract
Background: Despite improving surgical techniques and achieving more often complete debulking procedures, certain patients with advanced-stage ovarian cancer still have a very poor prognosis. The aim of the current paper is to investigate whether inflammatory and nutritional status can predict the long-term outcomes [...] Read more.
Background: Despite improving surgical techniques and achieving more often complete debulking procedures, certain patients with advanced-stage ovarian cancer still have a very poor prognosis. The aim of the current paper is to investigate whether inflammatory and nutritional status can predict the long-term outcomes of ovarian cancer patients. Methods: A retrospective analysis of 57 cases diagnosed with advanced-stage ovarian cancer submitted to surgery as first intent therapy was carried out. In all cases, the preoperative status was determined by calculating the CRP/albumin ratio, as well as the Glasgow score, the modified Glasgow score and the prognostic nutritional index. Results: Patients presenting higher values of the CRP/albumin ratio, with a higher Glasgow score, modified Glasgow score and prognostic nutritional index (PNI), were more frequently associated with incomplete debulking surgery, a higher peritoneal carcinomatosis index and poorer overall survival (20 months versus 9 months for the CRP/albumin ratio p = 0.011, 42 versus 27 versus 12 months for the Glasgow score p = 0.042, 50 versus 19 versus 12 months for the modified Glasgow score, p = 0.001, and 54 months versus 21 months, p = 0.011 for the prognostic nutritional index). Conclusions: A strong relationship between the nutritional and inflammatory status in advanced-stage ovarian cancer seems to exist. Full article
(This article belongs to the Special Issue Surgical Innovations in Gynecologic Oncology: Endometrial and Ovarian Cancer)
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Figure 1

Figure 1
<p>The ROC curve for the CRP/albumin ratio establishes a cut-off value of 0.05. Based on the cut-off value of 0.05 for the CRP/albumin ratio, patients were further classified into two categories: cases with a CRP/albumin ratio &lt; 0.05—9 patients—and cases with a CRP/albumin ratio &gt; 0.05—48 cases.</p> Full article ">Figure 2
<p>The mean overall survival was 9.36 months (confidence interval 4–11 months) for cases with CRP/alb &gt; 0.05 (blue line) and 20.49 months (confidence interval 22–26 months) for cases with CRP/alb &lt; 0.05 (red line) while the median overall survival was 8 months for cases with CRP/alb &gt; 0.05 (blue line) and 26 months for those with CRP/alb &lt; 0.05 (red line), <span class="html-italic">p</span> = 0.019.</p> Full article ">Figure 3
<p>The mean disease-free survival interval was 6 months for cases with CRP/alb &gt; 0.05 (blue line) and 14 months for cases with CRP/alb &lt; 0.05 (red line), <span class="html-italic">p</span> = 0.01.</p> Full article ">Figure 4
<p>Kaplan–Meier survival curves demonstrating a significant benefit of disease-free survival among patients scored with 0 or 1 point on the Glasgow score when compared to those with a Glasgow score of 2 points.</p> Full article ">Figure 5
<p>Kaplan–Meier survival curves demonstrating a significant benefit of the overall survival among patients scored 0 or 1 point when compared to those with a Glasgow score of 2 points.</p> Full article ">Figure 6
<p>Kaplan–Meier graphic showing the disease-free survival curves for patients with MGS = 0 (blue line), MGS = 1 (red line) and MGS = 2 (green line) and demonstrating a significant benefit in terms of progression-free survival for cases with MGS 0 or 1 (30 months versus 13 months versus 6 months, <span class="html-italic">p</span> = 0.01).</p> Full article ">Figure 7
<p>Kaplan–Meier graphic showing a significant benefit in terms of survival for patients with MGS 0 and 1 when compared to cases with MGS = 2 (50 months versus 19 months versus 12 months, <span class="html-italic">p</span> = 0.01).</p> Full article ">Figure 8
<p>Kaplan–Meier disease-free survival curves demonstrate a significant benefit among cases with PNI = 0 (red line) versus cases with PNI = 1 (blue line). The mean disease-free survival interval was 35 months among cases with preoperative PNI = 0 and, respectively, 11 months for cases with preoperative PNI = 1 (<span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 9
<p>Kaplan–Meier overall survival curves demonstrate a significant benefit in terms of survival for cases with PNI = 0 (red line) versus cases with PNI = 1 (blue line). Therefore, the overall survival rate was 54 months for cases with preoperative PNI = 0 and, respectively, 21 months for cases with preoperative PNI = 1 (<span class="html-italic">p</span> &lt; 0.001).</p> Full article ">

Other

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14 pages, 1325 KiB  
Systematic Review
Predicting Response to Treatment and Survival in Advanced Ovarian Cancer Using Machine Learning and Radiomics: A Systematic Review
by Sabrina Piedimonte, Mariam Mohamed, Gabriela Rosa, Brigit Gerstl and Danielle Vicus
Cancers 2025, 17(3), 336; https://doi.org/10.3390/cancers17030336 - 21 Jan 2025
Viewed by 157
Abstract
Background and Objective: Machine learning and radiomics (ML/RM) are gaining interest in ovarian cancer (OC) but only a few studies have used these methods to predict treatment response. The objective of this study was to review the literature on the applications of ML/RM [...] Read more.
Background and Objective: Machine learning and radiomics (ML/RM) are gaining interest in ovarian cancer (OC) but only a few studies have used these methods to predict treatment response. The objective of this study was to review the literature on the applications of ML/RM in OC assessments, specifically focusing on studies describing algorithms to predict treatment response and survival. Methods: This is a systematic review of the published literature from January 1985 to December 2023 on the use of ML/RM in OC An extensive search of electronic library databases was conducted. Two independent reviewers screened the articles initially by title then by full text. Quality was assessed using the MINORS criteria. p-values were generated using the Pearson’s Chi-squared (x2) test to compare the performances of ML/RM models with traditional statistics. Results: Of the 5576 screened articles, 225 studies were included. Between 2021 and 2023, 49 studies were published, highlighting the rapidly growing interest in ML/RM. Median-quality scores using the MINORS scale were similar between studies published between 1985–2021 and 2021–2023 (both 8). Neural Networks (22.6%) and LASSO (15.3%) were the most common ML/RM algorithms in OC. Among these studies, 13 focused specifically on prediction of treatment response using radiomics. A total of 5113 patients were analyzed. The most common algorithms were Random Forest (4/13) followed by Neural Networks (3/13) and Support Vectors (3/13). Radiomic analysis was used to predict response to neoadjuvant chemotherapy in seven studies, with a median AUC of 0.77 (range 0.72–0.93), while the median AUC was 0.82 (range 0.77–0.89) in the six studies assessing the prediction of optimal or complete cytoreduction. Median model accuracy reported in 7/13 studies was 73% (range 66–98%). Additionally, four studies investigated the use of ML/RM for survival prediction for OC. The XGBoost model had 80.9% accuracy in predicting 5-year survival compared to linear regression, which achieved 79% accuracy. The Random Forest model has 93.7% accuracy in predicting 12-month progression-free survival, compared to 82% for linear regression. Conclusions: In conclusion, we found that the use of ML/RM algorithms is becoming a more frequent method to predict responses to treatment of OC. These models should be validated in a prospective multicenter trial prior to integration into clinical use. Full article
(This article belongs to the Special Issue Surgical Innovations in Gynecologic Oncology: Endometrial and Ovarian Cancer)
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Figure 1

Figure 1
<p>PRISMA flow diagram of the study selection process.</p> Full article ">Figure 2
<p>Common applications of machine learning in ovarian cancer among 224 studies.</p> Full article ">Figure 3
<p>Pie chart of all ML techniques described in the included studies (<span class="html-italic">n</span> = 51) (others: UA&amp;MLR, CART, Elastic Net, naïve Bayes (NB), multi-layer perceptron (MLP), MLDTA, gradient-boosting machines (GBMs), MAC-Net, ELM, LDA, ResNet, Cox regression, SGD, DSS, MVA, K-mean clustering, and SE-SPP-DenseNet).</p> Full article ">
29 pages, 2343 KiB  
Systematic Review
Surgical Treatment for Endometrial Cancer, Hysterectomy Performed via Minimally Invasive Routes Compared with Open Surgery: A Systematic Review and Network Meta-Analysis
by Purushothaman Natarajan, Gayathri Delanerolle, Lucy Dobson, Cong Xu, Yutian Zeng, Xuan Yu, Kathleen Marston, Thuan Phan, Fiona Choi, Vanya Barzilova, Simon G. Powell, James Wyatt, Sian Taylor, Jian Qing Shi and Dharani K. Hapangama
Cancers 2024, 16(10), 1860; https://doi.org/10.3390/cancers16101860 - 13 May 2024
Cited by 1 | Viewed by 3070
Abstract
Background: Total hysterectomy with bilateral salpingo-oophorectomy via minimally invasive surgery (MIS) has emerged as the standard of care for early-stage endometrial cancer (EC). Prior systematic reviews and meta-analyses have focused on outcomes reported solely from randomised controlled trials (RCTs), overlooking valuable data [...] Read more.
Background: Total hysterectomy with bilateral salpingo-oophorectomy via minimally invasive surgery (MIS) has emerged as the standard of care for early-stage endometrial cancer (EC). Prior systematic reviews and meta-analyses have focused on outcomes reported solely from randomised controlled trials (RCTs), overlooking valuable data from non-randomised studies. This inaugural systematic review and network meta-analysis comprehensively compares clinical and oncological outcomes between MIS and open surgery for early-stage EC, incorporating evidence from randomised and non-randomised studies. Methods: This study was prospectively registered on PROSPERO (CRD42020186959). All original research of any experimental design reporting clinical and oncological outcomes of surgical treatment for endometrial cancer was included. Study selection was restricted to English-language peer-reviewed journal articles published 1 January 1995–31 December 2021. A Bayesian network meta-analysis was conducted. Results: A total of 99 studies were included in the network meta-analysis, comprising 181,716 women and 14 outcomes. Compared with open surgery, laparoscopic and robotic-assisted surgery demonstrated reduced blood loss and length of hospital stay but increased operating time. Compared with laparoscopic surgery, robotic-assisted surgery was associated with a significant reduction in ileus (OR = 0.40, 95% CrI: 0.17–0.87) and total intra-operative complications (OR = 0.38, 95% CrI: 0.17–0.75) as well as a higher disease-free survival (OR = 2.45, 95% CrI: 1.04–6.34). Conclusions: For treating early endometrial cancer, minimal-access surgery via robotic-assisted or laparoscopic techniques appears safer and more efficacious than open surgery. Robotic-assisted surgery is associated with fewer complications and favourable oncological outcomes. Full article
(This article belongs to the Special Issue Surgical Innovations in Gynecologic Oncology: Endometrial and Ovarian Cancer)
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Figure 1

Figure 1
<p>The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) diagram.</p> Full article ">Figure 2
<p>Results of the node-split analysis checking consistency and assumptions. The effect sizes and 95% credible intervals from direct comparison, indirect comparison and the network combining the two are shown in <a href="#cancers-16-01860-f002" class="html-fig">Figure 2</a>. The <span class="html-italic">p</span>-values in this context were used to test the consistency between direct and indirect comparisons. <a href="#cancers-16-01860-f002" class="html-fig">Figure 2</a> demonstrates that the consistency assumption is generally satisfied for 11 outcomes. The remaining three outcomes, fever, disease-free survival and total-intraoperative complications, were not shown due to insufficient data. (LRS: laparoscopic surgery, OS: open surgery, RS: robotic surgery).</p> Full article ">Figure 3
<p>Shows the forest plots of the 14 outcomes, providing the pooled estimates of the effect size of each surgery technique compared to open surgery (OS). (LRS: laparoscopic surgery, OS: open surgery, RS: robotic surgery).</p> Full article ">Figure 4
<p>Forest plots of the Bayesian network meta-analyses for each of the 14 outcomes.</p> Full article ">
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