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Background Ovarian cancer (OC) is a highly aggressive malignancy characterized by early dissemination of cancer cells from the surface of the ovary to the peritoneum. To gain a deeper understanding of the mechanisms associated with this intraperitoneal spread, we aimed to characterize the role of extracellular vesicles (EVs) in metastatic colonization in OC. Methods To this purpose, a total of 150 samples of ascitic fluids, blood serum, tumor and normal tissues from 60 OC patients, were extensively analyzed to characterize the EVs released in blood and ascitic fluids of OC patients, in terms of size, expression of superficial epitopes and abundance of miRNAs biocargo. Results A statistically significant difference in the size of EVs derived from ascitic fluid and serum was identified. Analysis of surface protein expression highlighted twenty epitopes with a significant difference between the two biological matrices, of which 18 were over- and two were under-expressed in ascitic fluid. With regard to miRNA levels, Principal Component Analysis (PCA) assessed four distinct clusters representing tumor tissue, normal tissue, ascitic fluid, and serum. A prominent difference in circulating miRNAs was observed in serum and ascitic fluid highlighting 98 miRNAs significantly deregulated ( P-adj  < 0.05) between the two bodily fluids. Deregulated miRNAs and epitopes underline an enrichment in ascites in components contributing to the metastatic spread. Conclusion The results highlight a clear difference between the two biological fluids, suggesting that tumor selectively releases specific EVs populations in serum or ascites. In this context, it seems that ascites-derived EVs play a major role in modulating EMT and metastatic cascade, which is a key feature of OC.

Background: Cervical dysplasia persistence/recurrence has a great impact on women’s health and quality of life. In this study, we investigated whether a prognostic nomogram may improve risk assessment after primary conization. Methods: This is a retrospective multi-institutional study based on charts of consecutive patients undergoing conization between 1 January 2010 and 31 December 2014. A nomogram assessing the importance of different variables was built. A cohort of patients treated between 1 January 2015 and 30 June 2016 was used to validate the nomogram. Results: A total of 2966 patients undergoing primary conization were analyzed. The median (range) patient age was 40 (18–89) years. At 5-year of follow-up, 6% of patients (175/2966) had developed a persistent/recurrent cervical dysplasia. Median (range) recurrence-free survival was 18 (5–52) months. Diagnosis of CIN3, presence of HR-HPV types, positive endocervical margins, HPV persistence, and the omission of HPV vaccination after conization increased significantly and independently of the risk of developing cervical dysplasia persistence/recurrence. A nomogram weighting the impact of all variables was built with a C-Index of 0.809. A dataset of 549 patients was used to validate the nomogram, with a C-index of 0.809. Conclusions: The present nomogram represents a useful tool for counseling women about their risk of persistence/recurrence after primary conization. HPV vaccination after conization is associated with a reduced risk of CIN2+.

Background/Objectives: The aim of this study was the early identification of endometriosis-associated ovarian cancer (EAOC) versus non-endometriosis associated ovarian cancer (NEOC) or non-cancerous tissues using pre-surgery contrast-enhanced-Computed Tomography (CE-CT) images in patients undergoing surgery for suspected ovarian cancer (OC). Methods: A prospective trial was designed to enroll patients undergoing surgery for suspected OC. Volumes of interest (VOIs) were semiautomatically segmented on CE-CT images and classified according to the histopathological results. The entire dataset was divided into training (70%), validation (10%), and testing (20%). A Python pipeline was developed using the transfer learning approach, adopting four different convolution neural networks (CNNs). Each architecture (i.e., VGG19, Xception, ResNet50, and DenseNet121) was trained on each of the axial slices of CE-CT images and refined using the validation dataset. The results of each CNN model for each slice within a VOI were combined using three rival machine learning (ML) models, i.e., Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbor (KNN), to obtain a final output distinguishing between EAOC and NEOC, and between EAOC/NEOC and non-tumoral tissues. Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. Each model’s performance was assessed based on the area under the receiver operating characteristic (ROC) curve (AUC), F1-score, sensitivity, and specificity. These metrics were then integrated into a Machine Learning Cumulative Performance Score (MLcps) to provide a comprehensive evaluation on the test dataset. Results: An MLcps value of 0.84 identified the VGG19 + majority voting ensemble as the optimal model for distinguishing EAOC from NEOC, achieving an AUC of 0.85 (95% CI: 0.70–0.98). In contrast, the VGG19 + SVM model, with an MLcps value of 0.76, yielded an AUC of 0.79 (95% CI: 0.63–0.93). For differentiating EAOC/NEOC from non-tumoral tissues, the VGG19 + SVM model demonstrated superior performance, with an MLcps value of 0.93 and an AUC of 0.97 (95% CI: 0.92–1.00). Conclusions: Hybrid models based on CE-CT have the potential to differentiate EAOC and NEOC patients as well as between OC (EAOC and NEOC) and non-tumoral ovaries, thus potentially supporting gynecological surgeons in personalized surgical approaches such as more conservative procedures.

Background: Primary prevention through vaccination is a prophylactic approach aiming to reduce the risk of developing human papillomavirus (HPV)-related lesions. No mature and long-term data supported the adoption of vaccination in women undergoing conization. Methods: This is a retrospective multi-institutional study. Charts of consecutive patients undergoing conization between 2010 and 2014 were collected. All patients included had at least 5 years of follow-up. We compared outcomes of patients undergoing conization plus vaccination and conization alone. A propensity-score matching algorithm was applied in order to reduce allocation biases. The risk of developing recurrence was estimated using Kaplan-Meir and Cox hazard models. Results: Overall, charts of 1914 women were analyzed. The study group included 116 (6.1%) and 1798 (93.9%) women undergoing conization plus vaccination and conization alone, respectively. Five-year recurrence rate was 1.7% (n = 2) and 5.7% (n = 102) after conization plus vaccination and conization alone, respectively (p = 0.068). After the application of a propensity-score matching, we selected 100 patients undergoing conization plus vaccination and 200 patients undergoing conization alone. The crude number of recurrences was 2 (2%) and 11 (5.5%) for patients undergoing conization plus vaccination and conization alone, respectively (p = 0.231). Vaccination had no impact on persistent lesions (no negative examination between conization and new cervical dysplasia; p = 0.603), but reduced the risk of recurrent disease (patients who had at least one negative examination between conization and the diagnosis of recurrent cervical dysplasia; p = 0.031). Conclusions: Patients having vaccination experience a slightly lower risk of recurrence than women who had not, although not statistically significantly different. Further evidence is needed to assess the cost effectiveness of adopting vaccination in this setting.

The European Society of Gynecologic Oncology/European Society of Radiation Therapy and Oncology/European Society of Pathology (ESGO/ESTRO/ESP) committee recently proposed a new risk stratification system for endometrial carcinoma (EC) patients that incorporates clinicopathologic and molecular features. The aim of the study is to compare the new ESGO/ESTRO/ESP risk classification system with the previous 2016 recommendations, evaluating the impact of molecular classification and defining a new algorithm for selecting cases for molecular analysis to assign the appropriate risk class. The cohort included 211 consecutive EC patients. Immunohistochemistry and next-generation sequencing were used to assign molecular subgroups of EC: mutant ( ), mismatch repair deficient (MMRd), p53 mutant (p53abn), and no specific molecular profile (NSMP). Immuno-molecular analysis was successful in all cases, identifying the four molecular subgroups: 7.6% , 32.2% MMRd, 20.9% p53abn, and 39.3% NSMP. The recent 2020 guidelines showed a 32.7% risk group change compared with the previous 2016 classification system: the reassignment is due to mutations, abnormal p53 expression, and a better definition of lymphovascular space invasion. The 2020 system assigns more patients to lower-risk groups (42.2%) than the 2016 recommendation (25.6%). Considering the 2020 risk classification system that includes the difference between \"unknown molecular classification\" and \"known,\" the integration of molecular subgroups allowed 6.6% of patients to be recategorized into a different risk class. In addition, the use of the proposed algorithm based on histopathologic parameters would have resulted in a 62.6% reduction in molecular analysis, compared to applying molecular classification to all patients. Application of the new 2020 risk classification integrating clinicopathologic and molecular parameters provided more accurate identification of low-and high-risk patients, potentially allowing a more specific selection of patients for post-operative adjuvant therapy. The proposed histopathologic algorithm significantly decreases the number of tests needed and could be a promising tool for cost reduction without compromising prognostic stratification.

Background Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the gastrointestinal tract and liver and peritoneum are the main sites of recurrence. Ovarian metastases from GIST are very rare. Case Presentation A 50 years-old woman was found to have a pelvic mass on transvaginal ultrasound (TV-US) and computed tomography (CT)-scan, considered as a right ovarian mass. The patient underwent surgical abdominal exploration that showed an ileal mass, a normal right ovary and an irregular and vascularized surface of the left ovary. A segmental ileal resection and an ileal anastomosis were performed. Frozen section showed a GIST and surgery was completed with hysterectomy, bilateral salpingo-oophorectomy, pelvic peritonectomy, peritoneal washing and Burch procedure. The histological examination confirmed an ileal GIST with ovarian metastases, harboring in both sites of disease a KIT exon 11 deletion. Conclusions Ovarian localizations, as far as rare, can be a clinical finding in case of ileal GIST patients, and both gynecologists, pathologists and medical oncologists should be able to recognize them.

Background The FSH starting dose is usually chosen according to women’s age, anamnesis, clinical criteria and markers of ovarian reserve. Currently used markers include antral follicle count (AFC), which is considered to have a very high performance in predicting ovarian response to FSH. The objective of the present study to elaborate a nomogram based on AFC for the calculation of the appropriate FSH starting dose in IVF cycles. Methods This is a retrospective study performed at the Mother-Infant Department of Modena University Hospital. IVF patients (n=505) were subjected to blood sampling and transvaginal ultrasound for measurement of serum day3 FSH, estradiol and AFC. The variables predictive of the number of retrieved oocytes were assessed by backwards stepwise multiple regression. The variables reaching the statistical significance were then used in the calculation for the final predictive model. Results A model based on age, AFC and FSH was able to accurately predict the ovarian sensitivity and accounted for 30% of the variability of ovarian response to FSH. An FSH dosage nomogram was constructed and overall it predicts a starting dose lower than 225 IU in 50.2% and 18.1% of patients younger and older than 35 years, respectively. Conclusions The daily FSH dose may be calculated on the basis of age and two markers of ovarian reserve, namely AFC and FSH, with the last two variables being the most significant predictors. The nomogram seems easily applicable during the daily clinical practice.

Epithelial ovarian cancer (EOC) is one of the most lethal cancers worldwide, mostly due to nonspecific symptoms and a lack of screening tests, which, taken together, contribute to delayed diagnosis and treatment. The current clinical biomarker is serum CA-125, which allows the identification of most advanced primary and relapsed disease and correlates with disease burden; however, as well highlighted in the literature, CA-125 often lacks sensitivity and specificity, and is not helpful in monitoring chemotherapeutic response or in predicting the risk of relapse. Given that, the identification of novel biomarkers able to foster more precise medical approaches and the personalization of patient management represents an unmet clinical requirement. In this context, circulating miRNAs may represent an interesting opportunity as they can be easily detected in all biological fluids. This is particularly relevant when looking for non-invasive approaches that can be repeated over time, with no pain and stress for the oncological patient. Given that, the present review aims to describe the circulating miRNAs currently identified as associated with therapeutic treatments in OC and presents a complete overview of the available evidence.

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.

Adenosarcomas are the rarest form of uterine sarcomas, and clinical experience with their management is still limited. Here, we reported 7 patients with uterine adenosarcoma referred to our institution, focusing on main pathologic features, their medical history, and long-term follow-up. Among these patients, we provided a detailed description of the medical history of a 49-year-old woman with advanced uterine adenosarcoma with sarcomatous overgrowth who presented a brilliant radiologic and pathologic response after 3 cycles of epirubicin and ifosfamide, ultimately achieving an extraordinary long-term outcome through an integrated surgical and medical approach. Our single-centre experience would suggest that aggressive uterine adenosarcomas with sarcomatous overgrowth are sensitive to standard epirubicin and ifosfamide and that an integrated approach, both medical and surgical, could be considered in clinical practice, again emphasizing the relevant role of multidisciplinary management for this extremely rare disease.