Learning curve for ultrasonographic diagnosis of deep infiltrating endometriosis using structured offline training program.

OBJECTIVE: To assess the learning curves of trainees during a structured offline/hands-on training program for the ultrasonographic diagnosis of deep infiltrating endometriosis (DIE). METHODS: Four trainees (all Ob/Gyn postgraduates with at least 5 years' experience in ultrasonography in obstetrics and gynecology, but with no experience of sonographic examination of DIE) participated in the study. They underwent a 2-week training program with a single trainer. Day 1 was devoted to theoretical issues and guided offline analysis of 10 three-dimensional ultrasound volumes. During the following days, four sessions of real-time sonographic examinations were performed in a DIE referral center ultrasound unit. In between these sessions, the trainees analyzed four datasets offline, each containing 25 volumes. At the end of each set, misinterpreted volumes were reassessed with the trainer. Presence or absence of DIE at surgery was considered the gold standard. The trainees' learning process was evaluated by learning-curve cumulative summation (LC-CUSUM) and the deviations of the trainees' level of performance at the control stage was assessed by CUSUM (standard CUSUM), for different locations of DIE. RESULTS: The trainees reached competence after an average of 17 (range, 14-21) evaluations for bladder, 40 (range, 30-60) for rectosigmoid, 25 (range, 14-34) for forniceal, 44 (range, 25-66) for uterosacral ligament (USL) and 21 (range, 14-43) for rectovaginal septum (RVS) locations of DIE, and then kept the process under control, with error levels of less than 4.5% until the end of the test. The overall accuracy for each trainee in diagnosis of DIE at the different locations ranged from 0.91 to 0.98 for bladder DIE, from 0.80 to 0.94 for rectosigmoid DIE, from 0.90 to 0.94 for forniceal DIE, from 0.79 to 0.82 for USL DIE and from 0.89 to 0.98 for RVS DIE. CONCLUSIONS: The suggested 2-week training program, based on a mixture of offline and live scanning sessions, is feasible and apparently provides effective training for the ultrasonographic diagnosis of DIE. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Sistemas de clasificación y predicción de riesgo de malignidad de las lesiones anexiales / Classification systems and prediction of risk of malignancy of the adnexal masses

El diagnóstico de una masa anexial es un problema frecuente en la consulta ginecológica. El principal objetivo de la evaluación de una masa anexial es el diagnóstico o exclusión de malignidad, dado que el cáncer de ovario es la neoplasia ginecológica más letal y que una cirugía inicial adecuada es uno de sus principales factores pronósticos. La evaluación ecográfica continúa siendo el mejor método para clasificar las masas anexiales. Si existe la sospecha de malignidad de la lesión, la paciente debe ser remitida a un centro especializado en ginecología oncológica. Sin embargo, si la sospecha es de benignidad, se puede optar por una actitud expectante o por un tratamiento quirúrgico conservador. El objetivo de este artículo es revisar los sistemas de clasificación de las lesiones anexiales más importantes y más ampliamente empleados, analizando su metodología y los resultados de su aplicación en los principales estudios de validación publicados hasta el momento

The diagnosis of an adnexal mass is a common problem in gynaecological consultation. The main objective of an adnexal mass evaluation is the diagnosis or exclusion of malignancy. This is the case because ovarian cancer is the most lethal gynaecological neoplasia and appropriate initial surgery is one of the main prognostic factors. Ultrasound scans continue to be the best method of classifying an adnexal mass. If there is any suspicion of a malignant tumour, the patient must be referred to a specialist gynaecological oncology centre. However, if there is any suspicion of a benign tumour, watchful waiting or minimally invasive surgery may be indicated. The objective of this article is to carry out a review of the most important and widely used classification systems of adnexal masses, analysing their methodology and the results of their application in the main validation studies published to date