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Simple Summary: Patients diagnosed with gynecological and breast cancer undergo multimodal treatments leading to estrogen deprivation and vaginal damage in case of radiotherapy, resulting in significant impairments of vulvo-vaginal function. Non-ablative intravaginal CO2 laser is a promising technique for VVA (vulvo-vaginal atrophy) in breast cancer, gynecological and other pelvic cancer survivors. In this study we explored the effectiveness and the long-term effects of repeated cycles of laser therapy. This therapeutic regimen could represent an effective treatment option for patients with limited therapeutic alternatives due to the hormone-sensitivity frequently showed by these cancers. This study aimed to investigate the efficacy and duration of multiple non-ablative intravaginal CO2 laser (V-lase®) cycles in breast cancer patients, gynecological and other pelvic cancers previously subjected to multiple oncological treatments. This prospective study enrolled women under the age of 65 years who reported vaginal symptoms. Data on the Vaginal Health Index (VHI), vaginal length (VL), vaginal pain measured using a Visual Analog Scale (VAS), and the Female Sexual Function Index (FSFI) were collected at baseline and before each laser application, and at subsequent follow-up visits. A total of 170 laser applications were performed on 113 women with various types of cancer. Most patients (57.5%) had received radiotherapy-based treatments before receiving laser treatment. Vaginal health parameters and sexual function improved significantly with each laser application. However, a temporary decline in these improvements occurred during the intervals between cycles. Such worsening was reversed with the subsequent cycle in all groups of patients, irrespective of the type of oncological treatments they had undergone. Multiple course vaginal laser therapy showed promising results as a potential treatment for vaginal atrophy in heavily treated gynecological and breast cancer patients, necessitating further research to determine the optimal time interval between cycles to ensure sustained positive effects. [ABSTRACT FROM AUTHOR]

Simple Summary: The differential diagnosis between uterine leiomyosarcomas and leiomyomas based on imaging represents one of the major challenges for gynecologists and radiologists. Currently, only histological examination can definitively resolve doubts in suspicious cases. The purpose of this study is to develop a machine learning model that can support clinical decision making. One of the proposed approaches, i.e., using the general linear model (GLM) classifier, has been patented by our team and has demonstrated good performance in retrospective analyses, with predicted area under the curve (AUC), sensitivity, and specificity on the test set ranging from 0.78 to 0.82, from 0.78 to 0.89, and from 0.67 to 0.87, respectively. The next step will involve validation at other medical centers and its prospective application. Background: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Methods: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. Results: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62–0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65–0.85). Conclusions: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions. [ABSTRACT FROM AUTHOR]

Objective: Neoadjuvant chemotherapy (NACT) represents a treatment option in patients with advanced epithelial ovarian cancer (AEOC) who are not good candidates for primary debulking surgery. Usually, 3 cycles of chemotherapy before surgery have been considered the best option for patient survival, although quite often some patients receive more than 3 cycles. The aim of this systematic review and meta-analysis was to identify the optimal number of NACT cycles reporting better survival in AEOC patients. Methods: PubMed, Cochrane Library, and Scopus were searched for original articles that analyzed the relationship between the number of chemotherapy cycles and clinical outcomes in AEOC patients before interval debulking surgery (IDS). The main outcomes were progression-free survival (PFS) and overall survival (OS). Results: A total of 22 studies comprising 7,005 patients diagnosed with AEOC were included in our analysis. In terms of survival, the reviewed studies dividing the patients in ≤3 NACT cycles vs. >3, showed a trend for a decrease in PFS and a significant reduction in OS with an increasing number of cycles, while a difference in both PFS and OS was revealed if early IDS included patients with 4 NACT cycles. These results should be interpreted with caution due to the complex characteristics of AEOC patients. Conclusion: In conclusion, our review and meta-analysis revealed that there is not enough evidence to determine the optimal number of NACT treatments before surgery. Further research in the form of well-designed randomized controlled trials is necessary to address this issue. [ABSTRACT FROM AUTHOR]

Endometrial cancer is the most common gynecological malignancy of the female reproductive organs. Historically it was divided into type I and type II, until 2013 when the Cancer Genome Atlas molecular classification was proposed. Here, we applied the different classification types on our endometrial cancer patient cohort in order to identify the most predictive one. We enrolled 117 endometrial cancer patients available for the study and collected the following parameters: age, body mass index, stage, menopause, Lynch syndrome status, parity, hypertension, type of localization of the lesion at hysteroscopy, type of surgery and complications, and presence of metachronous or synchronous tumors. The tumors were classified according to the European Society for Medical Oncology, Proactive Molecular Risk Classifier for Endometrial Cancer, Post-Operative Radiation Therapy in Endometrial Carcinoma, and Cancer Genome Atlas classification schemes. Our data confirmed that European Society for Medical Oncology risk was the strongest predictor of prognosis in our cohort. The parameters correlated with poor prognosis were the histotype, FIGO stage, and grade. Our study cohort shows that risk stratification should be based on the integration of histologic, clinical, and molecular parameters. [ABSTRACT FROM AUTHOR]