Prediction Models of Adnexal Masses: State-of-the-Art Review

Application of O-RADS US combined with MV-Flow to diagnose ovarian-adnexal tumors

PURPOSE

This study aimed to explore the application of Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) combined with MV-Flow (Samsung Medison Co., Ltd.) to diagnose ovarian-adnexal masses.

METHODS

A total of 112 ovarian-adnexal masses (81 benign and 31 malignant) from 105 consecutive patients were analyzed. The O-RADS US and vascular index from MV-Flow (VIMV) were measured and compared with the reference standard. O-RADS US and MV-Flow were tested for consistency.

RESULTS

Receiver operating characteristic curves were drawn for O-RADS US, MV-Flow, and their combination. The combined methods had the largest area under the curve (0.955), followed by O-RADS US (0.929) and MV-Flow (0.923). A mass was considered malignant when the O-RADS US classification was 5 and VIMV was ≥7.15. With this definition, MV-Flow had the highest sensitivity (87.10%), with consistent findings for the combined diagnostic methods and O-RADS US (83.87%). The specificity of the combined diagnostic methods (93.83%) was higher than that of MV-Flow (91.36%). O-RADS US had the lowest specificity (90.12%). The combined diagnostic methods had the highest coincidence rate (91.07%), and MV-Flow (90.18%) had a significantly higher coincidence rate than O-RADS US (88.39%). Both O-RADS US and MV-Flow showed good consistency among different physicians (former kappa, 0.974; latter intraclass correlation coefficient [ICC], 0.986). MV-Flow had a high consistency for the same physician (ICC, 1).

CONCLUSION

O-RADS US and MV-Flow exhibited good diagnostic efficacy, and their combined diagnostic efficacy was higher than that of each individually. O-RADS US and MV-Flow can improve the diagnosis of benign and malignant ovarian-adnexal masses.

Prediction model of adnexal masses with complex ultrasound morphology

Background

Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.

Methods

In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).

Results

Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.

Conclusion

The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.