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.

Novelties of ultrasound imaging for endometrial cancer preoperative workup.

INTRODUCTION: Endometrial cancer (EC) is the most frequent gynecological cancer. Transvaginal ultrasound (TVU) plays a leading role in the preoperative workup and often is the first diagnostic instrumental examination. Despite expert hands' ultrasound is recommended to assess myometrial invasion in early stage EC, this method is a strictly operator-dependent examination, and varying degrees of sensitivity and specificity have been reported. The present review aims to provide an update of ultrasound imaging in the preoperative work-up for EC patients. EVIDENCE ACQUISITION: A double-blind search was performed from May to September 2020. The following keywords: "ultrasound," "transvaginal ultrasound" and "endometrial cancer" were searched in Pubmed search engines, Scopus, and Web of Science. The Prisma statement was followed for the selection of the articles included. EVIDENCE SYNTHESIS: The initial search provided 958 studies, of which 11 were included in the analysis. non-English articles, not relevant to the purposes of this study, case reports and articles with fewer than 40 cases were excluded. CONCLUSIONS: TVU sensitivity and specificity in myometrial infiltration and cervical invasion is comparable to MRI but has lower costs, greater patient tolerability, and does not require contrast agents. An expert operator should perform the ultrasound examination in patients with suspected EC The presence of myometrial lesions, such as leiomyomas, could lower the diagnostic accuracy of ultrasound, so special attention should be paid to patients with concomitant uterine lesions.