Development and diagnostic performance of a modified O-RADS model incorporating septa number and blood flow for adnexal lesions.
The modified O-RADS model shows promise in stratifying malignancy risk in adnexal masses, potentially reducing unnecessary surgeries.
Where it sits
this study against the rest of the cjc-1295 corpusSummary and findings
This study assessed the diagnostic performance of a modified O-RADS model for adnexal lesions in 723 patients. The model incorporated septa number and blood flow to improve classification accuracy. The malignancy rates for different categories were reported.
Abstract
<h4>Background</h4>Ovarian tumours are common among women, with ultrasound serving as the primary diagnostic tool. The O-RADS system classifies tumours into categories 1-5 based on malignancy risk. Category 4 tumours are the most challenging to diagnose, as they may be either benign or malignant, often leading to unnecessary surgery.<h4>Methods</h4>This prospective study enrolled 723 patients with pathologically confirmed adnexal masses from July 2023 to May 2026. A total of 805 masses were included. All underwent standardised ultrasound with dynamic cine loop storage. Category 4 masses were subclassified into 4a (low-risk) and 4b (high-risk) based on locule count (>10 as high-risk) and colour score. Pathological results served as the gold standard.<h4>Results</h4>Among 225 O-RADS Category 4 masses, the malignancy rate was 19.1% (43/225). The 4b subclass (86 masses) had a malignancy rate of 45.3% (39/86), significantly higher than the 4a subclass (139 masses, 2.9% (4/139)). Masses with >10 locules showed a 53.3% malignancy rate, while those with colour score 3-4 had a 38.6% malignancy rate. The combined model achieved an area under the curve of 0.956, significantly outperforming single predictors. Decision curve analysis indicated that this model could reduce unnecessary surgeries by approximately 18%.<h4>Conclusion</h4>The modified O-RADS model effectively stratifies malignancy risk in adnexal masses, improving diagnostic accuracy and potentially reducing unnecessary surgical interventions.
Background
This paper addresses the challenge of diagnosing ovarian tumors, particularly those classified as O-RADS Category 4, which can be either benign or malignant. Prior knowledge indicates that misdiagnosis can lead to unnecessary surgeries. The study aims to enhance diagnostic accuracy by modifying the existing O-RADS model.
Methods
This prospective study enrolled 723 patients with pathologically confirmed adnexal masses from July 2023 to May 2026. A total of 805 masses were included, all undergoing standardized ultrasound with dynamic cine loop storage. The study subclassified Category 4 masses into 4a and 4b based on locule count and color score, using pathological results as the gold standard.
Results
Among 225 O-RADS Category 4 masses, the malignancy rate was 19.1% (43/225). The 4b subclass had a malignancy rate of 45.3% (39/86), significantly higher than the 4a subclass at 2.9% (4/139). Masses with >10 locules showed a malignancy rate of 53.3%, while those with a color score of 3-4 had a malignancy rate of 38.6%. The combined model achieved an area under the curve of 0.956.
Interpretation
The findings suggest that the modified O-RADS model improves diagnostic accuracy for adnexal masses compared to single predictors. However, while the statistical significance is clear, the clinical relevance of the effect sizes, particularly in the context of reducing unnecessary surgeries, should be evaluated further. Limitations such as the observational design and reliance on ultrasound may confound the conclusions.
Key findings
- Malignancy rate of 19.1% among 225 O-RADS Category 4 masses, n=225.
- Malignancy rate of 45.3% in the 4b subclass (86 masses), n=86.
- Malignancy rate of 2.9% in the 4a subclass (139 masses), n=139.
- Masses with >10 locules had a malignancy rate of 53.3%, n=not reported in abstract.
- Masses with colour score 3-4 had a malignancy rate of 38.6%, n=not reported in abstract.
- Combined model achieved an area under the curve of 0.956.
Limitations
- Observational study design.
- Reliance on ultrasound as a diagnostic tool.
- Single-site study may limit generalizability.
- Not reported in abstract.