Is the Risk of Ovarian Malignancy Algorithm Better Than Other Tests for Predicting Ovarian Malignancy in Women with Pelvic Masses?

AIM: The study aimed to compare the risk of ovarian malignancy algorithm (ROMA) with risk of malignancy index (RMI), cancer antigen 125 (CA125), human epididymis protein 4 (HE4) and Sassone ultrasonography (US) score in predicting ovarian cancer (OC) in women who present with pelvic or adnexal masses. METHODS: Pelvic US, serum CA125 and HE4 levels were investigated preoperatively in consecutively enrolled Thai women over 18 years with clinically diagnosed pelvic or adnexal masses who were undergoing elective surgery at a super tertiary hospital in Thailand in 2012. ROMA, RMI and Sassone US score were calculated. RESULTS: Of 260 women who were evaluated, 74 had OC. Areas under the receiver operating characteristic curve (AUC) in predicting OC were ROMA, 86.2%; RMI, 87.6%; CA125, 80.6%; HE4, 82.4%; and Sassone score, 77.1%. ROMA and RMI performed similarly well (AUCs 84.4 and 85.6%) in premenopausal women but RMI outperformed ROMA in postmenopausal women (AUCs 87.9 and 84.0%, respectively). At the conventional cutoff value, ROMA and CA125 showed the highest sensitivity (83.8%) but HE4 had the highest specificity (86.0%). CONCLUSIONS: ROMA was comparable to RMI in predicting OC. Either ROMA or RMI can be applied to women with pelvic masses, stratified into low- and high-risk groups for OCs.

Rajavithi-ovarian cancer predictive score (R-OPS): A new scoring system for predicting ovarian malignancy in women presenting with a pelvic mass.

OBJECTIVE: To develop a new scoring system based on menopausal status, ultrasound (US) findings, serum cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) to predict ovarian cancer (OC) in women presenting with a pelvic or adnexal mass. METHODS: Consecutive female patients aged over 18years with pelvic or adnexal masses investigated preoperatively by pelvic US, serum CA125 and HE4 who underwent elective surgery were enrolled. The "Rajavithi-Ovarian Cancer Predictive Score (R-OPS)" was developed using data from 2012 and validated using data from 2013 to 2014. The diagnosis of OC was based on pathological findings. Data were analyzed by logistic regression and area under the receiver operating characteristic curve (ROC-AUC). RESULTS: Based on a development set of 260 women including 74 with OC, menopausal status (M), serum CA125 and HE4, and US findings of solid lesions (U) were identified as significant predictors of OC. R-OPS=M×U×(CA125×HE4)(1/2) revealed good calibration (goodness-of-fit test, p=0.972) and discrimination (ROC-AUC=92.8%). Performance validation in 266 women, 82 with OC, showed good discrimination with ROC-AUC of 94.9%. Performance in the validation sample with a cutoff value of R-OPS>330 revealed sensitivity, specificity, and positive and negative predictive values of 93.9%, 79.9%, 67.5%, and 96.7%, respectively. CONCLUSIONS: The new R-OPS scoring system showed good discrimination between non-cancerous lesions and OC. However, a prospective study in a different setting is required to confirm these preliminary data.