Primary Vaginal Mucinous Adenocarcinoma of Intestinal Type-Clinical, Radiological and Morphological Aspects.

Background and Objectives: Neoplasms of the vagina are rare and account for 1-2% of all tumors of the female reproductive system. Primary neoplasms of the vagina are most often carcinomas originating from squamous or glandular epithelium. Of the primary glandular tumors, clear cell, endometrioid, and serous adenocarcinomas are the most common types, while mucinous and mesonephric types are very rare. Mucinous adenocarcinoma is histologically subclassified into endocervical and intestinal types. We add to the existing literature another case of an extremely rare gynecological neoplasm-primary vaginal mucinous adenocarcinoma (PVMAC) intestinal type associated with vaginal villous adenoma with high-grade dysplasia. We discuss the clinical, radiological and morphological features of this rare entity. Materials and Methods: We report a case of a 59-year-old woman with PVMAC intestinal type associated with vaginal villous adenoma with high-grade dysplasia. The patient was evaluated with a gynecological exam, and biopsy, curettage and tumor excision were performed. The positron emission tomography-computed tomography (PET/CT) scan, at the level of the pelvis, supported the primary location of the disease. Histological and immunohistochemical methods were applied. Results: The gynecological examination of the vagina revealed an exophytic polypoid mass with a diameter of 3 cm, located on the posterior wall, in the area of introitus vaginae. The PET/CT scan revealed a hypermetabolic malignant formation involving the vagina and anal canal, without evidence of pelvic and inguinal lymphadenopathy, and also, it excluded disease at sites other than the vagina. The histological and immunohistochemical investigations, as well as the clinical and radiological data, lent support to the diagnosis "primary vaginal mucinous adenocarcinoma intestinal type". Conclusions: PVMAC intestinal type is a rare gynecological pathology, which presents a serious challenge for oncogynecologists, radiologists and pathologists.

A very rare case of echinococcus granulosus arising in the ovary and the uterus.

Hydatidosis is a zoonosis caused by the larval form of a cestode of the species Echinococcus granulosus, the canine tapeworm. It can affect any organ of the human body except nails, hair, and cornea. It most often involves the liver, lungs, muscles, and bones. Involvement of organs in the small pelvis is extremely rare, mainly secondary to dissemination of hydatidosis from another organ, and the ovaries are most often involved, followed by the uterus. We present a 41-year-old Caucasian woman with complaints of mild pain, heaviness in the small pelvis, and a fibroid detected by ultrasound examination. She had reported for 2 previous operations for echinococcus cysts, and we found cystic formations in the liver and pelvis by computed tomography. It was decided that it is a recurrence of echinococcosis with a new cyst both in ovary and uterus. A hysterectomy with adnexectomy was performed, and one year later there is no evidence of recurrence of echinococcosis. In the presence of a formation in the small pelvis, one should always think about echinococcosis, especially if there is evidence of previous manifestations of the disease. Diagnosing this disease is extremely important for proper treatment. As rare as uterine involvement is, this possibility should not be overlooked.

Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis.

OBJECTIVE: To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. METHODS: A multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). RESULTS: Of 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. CONCLUSION: The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.