Preoperative and intraoperative assessment of myometrial invasion in patients with FIGO stage I non-endometrioid endometrial carcinoma-a large-scale, multi-center, and retrospective study.

INTRODUCTION: Myometrial invasion is a prognostic factor for lymph node metastases and decreased survival in non-endometrioid endometrial carcinoma patients. Herein, we explored the mode of myometrial invasion diagnosis in FIGO stage I non-endometrioid carcinoma and evaluated the differences in diagnostic efficiency among intraoperative frozen section (IFS), intraoperative gross examination (IGE), magnetic resonance imaging (MRI), and computed tomography (CT) in clinical practice. Finally, we suggested which test should be routinely performed. METHOD: This was a historical cohort study nationwide with 30 centers in China between January 2000 and December 2019. Clinical data, including age, histology, method of myometrial invasion evaluation (MRI, CT, IGE, and IFS), and final diagnosis of postoperative paraffin sections, were collected from 490 non-endometrioid endometrial carcinoma (serous, clear cell, undifferentiated, mixed carcinoma, and carcinosarcoma) women in FIGO stage I. RESULTS: Among the 490 patients, 89.59% presented myometrial invasion. The methods reported for myometrial invasion assessment were IFS in 23.47%, IGE in 69.59%, MRI in 37.96%, and CT in 10.20% of cases. The highest concordance was detected between IFS and postoperative paraffin sections (Kappa = 0.631, accuracy = 93.04%), followed by IGE (Kappa = 0.303, accuracy = 82.40%), MRI (Kappa = 0.131, accuracy = 69.35%), and CT (Kappa = 0.118, accuracy = 50.00%). A stable diagnostic agreement between IFS and the final results was also found through the years (2000-2012: Kappa = 0.776; 2013-2014: Kappa = 0.625; 2015-2016: Kappa = 0.545; 2017-2019: Kappa = 0.652). CONCLUSION: In China, the assessment of myometrial invasion in non-endometrioid endometrial carcinoma is often performed via IGE, but the reliability is relatively low in contrast to IFS. In clinical practice, IFS is a reliable method that can help accurately assess myometrial invasion and intraoperative decision-making (lymph node dissection or not). Hence, it should be routinely performed in non-endometrioid endometrial carcinoma patients.

Predicting the success of vaginal surgery: a quantitative risk assessment model for future investigation.

OBJECTIVE: To introduce a model incorporating expected risks for a vaginal procedure based on objective measurements of a patient's characteristics and propose it as a potential tool to assist in the selection of candidates for vaginal surgery. STUDY DESIGN: A quantitative model consisting of 13 clinical variables identified as risk factors in a prospective vaginal procedure was developed. Medical records of 315 women undergoing a set of routine gynecological surgeries via the vaginal, laparoscopic, and abdominal routes were obtained during January 2010 and November 2011. These surgeries included hysterectomy, myomectomy, bilateral or unilateral salpingo-oophorectomy and adnexal cystectomy. After that, each patient was scored according to the model. Sensitivity and specificity of the model were analyzed in one data set (cohort I) by receiver operating characteristic (ROC) curve and independently validated in a second data set (cohort II). RESULTS: 175 patients were included in cohort I while the other 140 patients formed cohort II. The intra- and post-operative complication rates were 0.6% and 0%, respectively. A vaginal procedure was predicted with good accuracy (AUC=0.852). The sensitivity was 86.0% and specificity was 72.0% at an optimal cut-off point of score=3. The predication accuracy of this model was further validated in cohort II and reached as high as 85.7%. Furthermore, the score was significantly associated with the volume of estimated blood loss and the duration of operation time (P<0.05). CONCLUSION: Our quantitative risk assessment model predicts safe vaginal surgery with good accuracy. Predictive tools based on such a model could help surgeons to optimize patient selection and thus contribute to reducing costs while enhancing patients' satisfaction. We invite other researchers to modify and validate the model in other populations.