Diagnostic Added-Value of Serum CA-125 on the IOTA Simple Rules and Derivation of Practical Combined Prediction Models (IOTA SR X CA-125)

The predictive value of nomogram for adnexal cystic-solid masses based on O-RADS US, clinical and laboratory indicators

BACKGROUND

Ovarian cancer remains a leading cause of death among women, largely due to its asymptomatic early stages and high mortality when diagnosed late. Early detection significantly improves survival rates, and the Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) is currently the most commonly used method, but has limitations in specificity and accuracy. While O-RADS US has standardized reporting, its sensitivity can lead to the misdiagnosis of benign masses as malignant, resulting in overtreatment. This study aimed to construct a nomogram model based on the O-RADS US and clinical and laboratory indicators to predict the malignancy risk of adnexal cystic-solid masses.

METHODS

This retrospective study collected data from patients with adnexal cystic-solid masses who underwent ultrasonography and were pathologically confirmed between January 2021 and December 2023 at the First Affiliated Hospital of Shenzhen University. They were categorized into benign and malignant groups according to pathological findings. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the most relevant predictors of ovarian cancer. A nomogram model was constructed, and its diagnostic performance was calculated. We bootstrapped the data 500 times to perform internal verification, drew a calibration curve to verify the prediction ability, and performed a decision curve analysis to assess clinical usefulness.

RESULTS

A total of 399 patients with adnexal cystic-solid masses were included in this study: 327 in the benign group and 72 in the malignant group. Five predictors associated with the risk of malignancy of adnexal cystic-solid masses were selected using LASSO regression: O-RADS, acoustic shadowing, postmenopausal status, CA125, and HE4. The area under the curve, sensitivity, specificity, accuracy, positive and negative predictive values of the nomogram were 0.909, 83.3%, 82.9%, 83.0%, 51.7%, and 95.8%, respectively. The calibration curve of the nomogram showed good consistency between the predicted and actual probabilities, and the decision curve showed good clinical usefulness.

CONCLUSION

The nomogram model based on O-RADS US and clinical and laboratory indicators can be used to predict the risk of malignancy in adnexal cystic-solid masses, with high predictive performance, good calibration, and clinical usefulness.

Diagnostic Accuracy of Biomarkers and International Ovarian Tumor Analysis Simple Rules in Diagnosis of Ovarian Cancer

This study investigated whether combining International Ovarian Tumor Analysis (IOTA) Simple Rules with tumor biomarkers would improve the diagnostic accuracy for early detection of adnexal malignancies. Receiver operating characteristic curve analysis of suspected adnexal tumors was performed in 226 women admitted for surgery at the University Clinical Center of Kosovo. Primary outcome was the diagnostic accuracy of the combination of adnexal mass biomarkers and IOTA Simple Rules. IOTA Simple Rules combined with biomarker indications increased the diagnostic accuracy of classifying adnexal masses. Data analysis of individual measures showed that ferritin had the lowest rate of sensitivity.

Biomarker-Based Models for Preoperative Assessment of Adnexal Mass: A Multicenter Validation Study

Ovarian cancer (OC) is the most lethal genital malignancy in women. We aimed to develop and validate new proteomic-based models for non-invasive diagnosis of OC. We also compared them to the modified Risk of Ovarian Malignancy Algorithm (ROMA-50), the Copenhagen Index (CPH-I) and our earlier Proteomic Model 2017. Biomarkers were assessed using bead-based multiplex technology (Luminex®) in 356 women (250 with malignant and 106 with benign ovarian tumors) from five European centers. The training cohort included 279 women from three centers, and the validation cohort 77 women from two other centers. Of six previously studied serum proteins (CA125, HE4, osteopontin [OPN], prolactin, leptin, and macrophage migration inhibitory factor [MIF]), four contributed significantly to the Proteomic Model 2021 (CA125, OPN, prolactin, MIF), while leptin and HE4 were omitted by the algorithm. The Proteomic Model 2021 revealed a c-index of 0.98 (95% CI 0.96, 0.99) in the training cohort; however, in the validation cohort it only achieved a c-index of 0.82 (95% CI 0.72, 0.91). Adding patient age to the Proteomic Model 2021 constituted the Combined Model 2021, with a c-index of 0.99 (95% CI 0.97, 1) in the training cohort and a c-index of 0.86 (95% CI 0.78, 0.95) in the validation cohort. The Full Combined Model 2021 (all six proteins with age) yielded a c-index of 0.98 (95% CI 0.97, 0.99) in the training cohort and a c-index of 0.89 (95% CI 0.81, 0.97) in the validation cohort. The validation of our previous Proteomic Model 2017, as well as the ROMA-50 and CPH-I revealed a c-index of 0.9 (95% CI 0.82, 0.97), 0.54 (95% CI 0.38, 0.69) and 0.92 (95% CI 0.85, 0.98), respectively. In postmenopausal women, the three newly developed models all achieved a specificity of 1.00, a positive predictive value (PPV) of 1.00, and a sensitivity of >0.9. Performance in women under 50 years of age (c-index below 0.6) or with normal CA125 (c-index close to 0.5) was poor. CA125 and OPN had the best discriminating power as single markers. In summary, the CPH-I, the two combined 2021 Models, and the Proteomic Model 2017 showed satisfactory diagnostic accuracies, with no clear superiority of either model. Notably, although combining values of only four proteins with age, the Combined Model 2021 performed comparably to the Full Combined Model 2021. The models confirmed their exceptional diagnostic performance in women aged ≥50. All models outperformed the ROMA-50.

Efficacy of IOTA simple rules, O-RADS, and CA125 to distinguish benign and malignant adnexal masses

Objective

Ovarian cancer is the most deadly deadliest gynecological tumor in the female reproductive system. Therefore, the present study sought to determine the diagnostic performance of International Ovarian Tumor Analysis Simple Rules (IOTA SR), the Ovarian-Adnexal Reporting and Data System (O-RADS), and Cancer Antigen 125 (CA125) in discriminating benign and malignant ovarian tumors. The study also assessed whether a combination of the two ultrasound categories systems and CA125 can improve the diagnostic performance.

Methods

A total of 453 patients diagnosed with ovarian tumors were retrospectively enrolled from Fujian Cancer Hospital between January 2017 and September 2020. The data collected from patients included age, maximum lesion diameter, location, histopathology, levels of CA125, and detailed ultrasound reports. Additionally, all ultrasound images were independently assessed by two ultrasound physicians with more than 5 years of experience in the field, according to the IOTA simple rules and O-RADS guidelines. Furthermore, the area under the curve (AUC), sensitivity, and specificity of the above mentioned predictors were calculated using the receiver operating characteristic curve.

Results

Out of the 453 patients, 184 had benign lesions, while 269 had malignant ovarian tumors. In addition, the AUCs of IOTA SR, O-RADS, and CA125 in the overall population were 0.831, 0.804, and 0.812, respectively, and the sensitivities of IOTA SR, O-RADS, and CA125 were 94.42, 94.42, and 80.30%, respectively. On the other hand, the AUCs of IOTA SR combined with CA125, O-RADS combined with CA125, and IOTA SR plus O-RADS combined with CA125 were 0.900, 0.891, and 0.909, respectively. The findings also showed that the AUCs of a combination of the three approaches were significantly higher than those of individual strategies (p<0.05) but not significantly higher than the AUC of a combination of two methods (p>0.05).

Conclusion

The findings showed that a combination of IOTA SR or O-RADS in combination with CA125 may improve the ability to distinguish benign from malignant ovarian tumors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00947-9.

Evaluation of the Diagnostic Value of the Ultrasound ADNEX Model for Benign and Malignant Ovarian Tumors

Objective

To investigate the diagnostic performance of the ADNEX model in the International Ovarian Tumor Analysis diagnostic models for ovarian tumors and further explore its application value in the staging of ovarian tumors.

Methods

A total of 224 patients who underwent ultrasound for evaluation of adnexal masses and were treated surgically owing to adnexal masses from January 2018 to June 2020 in our hospital were selected for research on the diagnostic accuracy of the ADNEX model. The clinical information and ultrasonographic findings of the patients were collected, and the pathological diagnosis was taken as the gold standard. According to the ADNEX model, the ovarian tumors were divided into five subtypes: benign and borderline, stage I, stage II–IV, and metastatic cancer. The sensitivity, specificity, positive predictive value, negative predictive value, diagnostic odds ratio, and area under the receiver operating characteristics curve (AUC) of the ADNEX model were calculated.

Results

Of the 224 patients, 119 (53.1%) developed benign tumors and 105 (46.9%) had malignant tumors. When the cut-off value for malignancy risk was 10%, the ADNEX model including CA 125 achieved a sensitivity of 94.3% (95% CI: 88.0–97.9%), specificity of 74.0% (95% CI: 65.1–81.6%), positive predictive value of 76.2% (95% CI: 70.2–81.3%), negative predictive value of 93.6% (95% CI: 87.0–97.0%), diagnostic odds ratio of 45.25, and an AUC of 0.94 (95% CI: 0.90–0.97) for differentiating between benign and malignant ovarian tumors. The AUC in the model excluding CA 125 was 0.93 (95% CI: 0.89–0.96), but the difference was not statistically significant (P=0.20). The accuracy of the ADNEX model for the diagnosis of ovarian tumors of all subtypes exceeds 80% when CA 125 measurements were included in the application, but the sensitivity for diagnosing borderline, stage I, and metastatic ovarian tumors was only 60.0% (95% CI:36.1–80.9%), 28.6% (95% CI:8.4–58.1%) and 45.5% (95% CI:16.7–76.6%).

Conclusion

The ADNEX model shows good diagnostic performance in differentiating between benign and malignant ovarian tumors. The model has a certain clinical value in the diagnosis of all subtypes of ovarian tumors, but the sensitivity is unsatisfactory for the diagnosis of borderline, stage I, and metastatic ovarian tumors and needs to be verified.