Tran Thao Nguyen Nguyen

Comparative diagnostic performance of the early‐stage ovarian malignancy score versus other risk prediction models in early‐stage ovarian cancer: A Vietnamese prospective cohort study

Abstract Objective This study compares the diagnostic performance of the early‐stage ovarian malignancy (EOM) score against other risk prediction models for identifying early‐stage ovarian cancer. Methods This prospective cohort study involved 925 cases from the obstetrics and gynecology departments of two tertiary hospitals from May 2018 to December 2023. The data included gynecologic examination and/or ultrasound findings, menopausal status, ultrasonography features, serum CA125, and HE4 values, which were used to calculate the EOM score and compare it with other algorithms. Preoperative predictions were validated against postoperative histopathological data. Results In total, 792 cases (85.62%) were benign tumors, 74 cases (8.00%) were identified as early‐stage ovarian cancer, and 59 cases (6.38%) were classified as advanced‐stage ovarian cancer. With a cut‐off of ≥13, the EOM score achieved an area under the curve (AUC) value of 0.908 for distinguishing between cancer and non‐cancer, demonstrating sensitivity of 83.46% and specificity of 82.90%. For early‐stage cancer, the EOM score had an AUC value of 0.843. The EOM score outperformed the risk of malignancy index, the risk of ovarian malignancy algorithm, CPH‐I, CA125, and HE4 ( P  < 0.05). Conclusion The EOM score is a straightforward and effective tool for predicting early‐stage ovarian cancer, yielded performance similar to the IOTA Simple Rules combined with CA125.

Diagnostic performances of the Ovarian Adnexal Reporting and Data System, the Risk of Ovarian Malignancy Algorithm, and the Copenhagen Index in the preoperative prediction of ovarian cancer: a prospective cohort study

This study aimed to assess the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA), Copenhagen Index (CPH-I), and Ovarian Adnexal Reporting and Data System (O-RADS) for the preoperative prediction of ovarian cancer (OC). A prospective cohort study was conducted on 462 patients diagnosed with ovarian tumors admitted to the Departments of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy Hospital, and Hue Central Hospital from May 2020 to December 2022. ROMA and CPH-I were calculated using cancer antigen 125 (CA125), human epididymal protein 4 (HE4) levels, and patient characteristics (age and menopausal status). O-RADS criteria were applied to describe ovarian tumor characteristics from ultrasound findings. Compared with histopathological results, the predictive values of ROMA, CPH-I, and O-RADS alone or in combination with CA125/HE4 for OC were calculated. Among 462 patients, 381 had benign tumors, 11 had borderline tumors, and 50 had OC. At optimal cut-off points, ROMA's and CPH-I's areas under the curves (AUCs) were 0.880 (95% confidence interval [CI]=0.846-0.909) and 0.890 (95% CI=0.857-0.918), respectively, and ROMA and CPH-I sensitivities/specificities (Se/Sp) were 68.85%/95.01% and 77.05%/91.08%, respectively. O-RADS ≥3 yielded an AUCs of 0.949 (95% CI=0.924-0.968), with Se/Sp of 88.52%/88.98% (p<0.001). Combining O-RADS with CA125 demonstrated the highest predictive value, with AUCs of 0.969 (95% CI=0.949-0.983) and Se/Sp of 98.36%/86.09% (p<0.001). The ROMA, CPH-I, O-RADS, O-RADS + CA125, and O-RADS + HE4 models demonstrated good predictive values for OC; the combination of O-RADS and CA125 yielded the highest values.

The Optimal Cut-Off Point of the Andex Model for the Prediction of the Ovarian Cancer Risk

Objective: This study aimed to assess the effectiveness and determine the optimal cut-off point of the ADNEX model in women presenting with a pelvic or adnexal tumor. Method: All women presented with adnexal mass and were scheduled for operation at Hue University of Medicine and Pharmacy Hospital and Hue Central Hospital, Vietnam during June 2019 – May 2021 were included and categorized according to their histopathologic reports into ovarian cancer groups and benign ovarian tumor groups. Multivariable logistic regression was used to explore for potential predictors. The ADNEX model with and without CA125 was used to assess the risk of ovarian cancer preoperative. The goldden standard to evaluate the accuracy of ultrasonography using the ADNEX model was the pathological report. In addition, the accuracy as well as optimum cut-off point of the ADNEX model was estimated with and without CA125. Results: A total of 461 participants were included in analysis and predictive model development, 65 patients in ovarian cancer group and 361 in benign tumor group. The ADNEX model combined with CA125 proved to be a useful predictor with an area under ROC of 0.961 (0.940 – 0.977) with Youden’s index of 0.8395, p < 0.001. The ADNEX model without CA125 also had high predictive value between benign and malignant tumors, with an area under ROC of 0.956 (0.933 – 0.973) with Youden’s index of 0.8551, p < 0.001. Cut-off of the ADNEX with CA125 was 13.5 and without CA125 was 13.1 for sensitivities were 90.8 (81.0 – 96.5) and 93.9 (85.0 – 97.5), specificities 93.2 (90.2 – 95.5) and 91.67 (88.5 – 94.2). The difference in the predictive value of malignancy-risk between the ADNEX model with CA125, without CA125 was not statistically significant, p=0.4883. Conclusion: The ADNEX model, with or without the combining marker CA 125, provides a valuable predictive value for ovarian tumor malignancy preoperative.

Screening for Ovarian Malignancy

Ovarian cancer is the second most common gynecologic malignancy. In 2008, it was the seventh leading cause of cancer deaths in women worldwide. Estimating the risk of malignancy is essential in the management of adnexal masses and several mathematical models and scoring systems have been developed to be used for discrimination between benign and malignant adnexal masses. Knowledge of the specific type of adnexal pathology before surgery is likely to improve patient triage with high accuracy, and it also makes it possible to optimize treatment. The correct identification of stage I cancer is particularly important