Laser Therapy in Heavily Treated Oncological Patients Improves Vaginal Health Parameters.

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.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.

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.

miRNA levels are associated with body mass index in endometrial cancer and may have implications for therapy.

Endometrial cancer (EC) is the most prevalent gynecological cancer in high-income countries. Its incidence is skyrocketing due to the increase in risk factors such as obesity, which represents a true pandemic. This study aimed to evaluate microRNA (miRNA) expression in obesity-related EC to identify potential associations between this specific cancer type and obesity. miRNA levels were analyzed in 84 EC patients stratified based on body mass index (BMI; ≥30 or <30) and nine noncancer women with obesity. The data were further tested in The Cancer Genome Atlas (TCGA) cohort, including 384 EC patients, 235 with BMI ≥30 and 149 with BMI <30. Prediction of miRNA targets and analysis of their expression were also performed to identify the potential epigenetic networks involved in obesity modulation. In the EC cohort, BMI ≥30 was significantly associated with 11 deregulated miRNAs. The topmost deregulated miRNAs were first analyzed in 84 EC samples by single miRNA assay and then tested in the TCGA dataset. This independent validation provided further confirmation about the significant difference of three miRNAs (miR-199a-5p, miR-449a, miR-449b-5p) in normal-weight EC patients versus EC patients with obesity, resulting significantly higher expressed in the latter. Moreover, the three miRNAs were significantly correlated with grade, histological type, and overall survival. Analysis of their target genes revealed that these miRNAs may regulate obesity-related pathways. In conclusion, we identified specific miRNAs associated with BMI that are potentially involved in modulating obesity-related pathways and that may provide novel implications for the clinical management of obese EC patients.