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.

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study.

BACKGROUND: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. METHODS: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). RESULTS: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. CONCLUSIONS: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.

Optimal number of neoadjuvant chemotherapy cycles prior to interval debulking surgery in advanced epithelial ovarian cancer: a systematic review and meta-analysis of progression-free survival and overall survival.

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. TRIAL REGISTRATION: PROSPERO Identifier: CRD42022334959.

Post-operative residual disease and number of cycles of neoadjuvant chemotherapy in advanced epithelial ovarian carcinoma.

BACKGROUND: The optimal number of neoadjuvant chemotherapy cycles in patients with advanced ovarian cancer is still disputed. OBJECTIVE: To evaluate the impact of the number of neoadjuvant chemotherapy cycles and role of optimal cytoreduction on the prognosis of patients with advanced ovarian cancer. METHODS: Clinical and pathological details were examined. Patients were evaluated combining the number of cycles of neoadjuvant chemotherapy-namely, 'interval debulking surgery' after up to four neoadjuvant chemotherapy cycles, and 'delayed debulking surgery' after more than four cycles of therapy. RESULTS: A total of 286 patients were included in the study. Complete cytoreduction with no residual peritoneal disease (CC0) was achieved in 74 (74%) patients with interval debulking surgery and 124 (66.7%) patients with delayed interval debulking. Of those with residual disease, there were 26/88 (29.5%) patients in the interval debulking surgery group and 62/88 (70.5%) patients in the delayed debulking surgery group. Comparison of patients with delayed debulking-CC0 and interval debulking-CC0 showed no difference in progression-free survival (p=0.3) or overall survival (p=0.4), while significantly worse outcomes were observed in patients with interval debulking-CC1 (p=0.02 and p=0.04, respectively). Specifically, patients with interval debulking-CC1 had an approximately 67% increased risk of disease progression (p=0.04; HR=2.01 (95% CI 1.04 to 4.18)) and a 69% higher risk of death than patients with delayed debulking-CC0 (p=0.03; HR=2.34 (95% CI 1.11 to 4.67)). CONCLUSION: Increasing the number of neoadjuvant chemotherapy cycles does not worsen patient outcomes if complete resection is achieved. Nevertheless, additional prospective trials are necessary to establish the optimum number of neoadjuvant chemotherapy cycles.

Electrochemotherapy with intravenous bleomycin for heavily pre-treated vulvar cancer patients.

OBJECTIVE: The management of vulvar cancer recurrences is complicated by patients' advanced age and comorbidities. Bleomycin-based electrochemotherapy is a potential treatment option in this setting. However, no data on long-term outcomes are available. Therefore, a multicenter observational study was designed to evaluate the 5-year results in these patients. METHODS: Data about patients and tumor characteristics, electrochemotherapy cycles, clinical response, and follow-up were recorded. Treatment procedures were performed according to the European Standard Operating Procedures of Electrochemotherapy (ESOPE) guidelines. Response was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. RESULTS: Fifty-one patients (mean age 82.31±7.28 years) with squamous cell vulvar cancer underwent electrochemotherapy (median number of sessions 1; range 1-4). 20 patients had complete response and 32% of these were disease-free after 2 years (median progression-free survival 16.8 months). In 13 patients with partial response the median progression-free survival was 15.36 months, while patients with stable or progressive disease showed tumor relapse after 6.95 and 3.26 months, respectively (p<0.001). Median overall survival was 18.77, 13.07, 6.73, and 11.13 months in patients with complete response, partial response, stable disease, and progressive disease, respectively (p=0.001). CONCLUSION: Long-term follow-up of vulvar cancer patients showed reasonable tumor control after electrochemotherapy and improved progression-free survival and overall survival in responder subjects compared with non-responders. Further studies aimed at improving local response after electrochemotherapy are warranted. Thus, this approach represents a potential alternative for these patients.