O-RADS Ultrasound Version 1: A Scenario-Based Review of Implementation Challenges

Gargan, Mary Louise; Frates, Mary C.; Benson, Carol B.; Guo, Yang

doi:10.2214/ajr.22.28061

Cited by 6 publications

(1 citation statement)

References 72 publications

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“…The cystic solidity, boundary, morphology, blood flow signal, internal echogenicity, protrusion, and contents of the mass were to symbolize each pelvic lesion that was placed in one of the categories (O-RADS 0-5) and stratify the risk according to the flowchart: (1) O-RADS category 0: lesions that cannot be fully evaluated using ultrasonography; (2) O-RADS category 1: physiological category, referring to normal premenopausal ovaries; (3) O-RADS category 2: almost certainly benign lesions (malignant risk <1%); (4) O-RADS 3 category: low-risk (malignant risk of ≥1, <10%); (5) O-RADS 4 category: intermediate risk (malignant risk of ≥10, <50%); (6) O-RADS 5 category: high risk (malignant risk of ≥50%) (17). In this study, the risk threshold for malignancy was identified as 10% (18) when comparing benign and malignant; that is, an O-RADS Category 4 or higher was defined as malignant.…”

Section: Model Reference Standards 2231 O-rads Score and Model Evalua...mentioning

confidence: 99%

Prediction model of adnexal masses with complex ultrasound morphology

Wu,

Miao,

Wang

et al. 2023

Front. Med.

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BackgroundBased 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.MethodsIn 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).ResultsFour 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.ConclusionThe 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.

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Section: Model Reference Standards 2231 O-rads Score and Model Evalua...mentioning

confidence: 99%