European radiology
August 30, 2026 · via Europe PMC
Improving diagnostic performance of O-RADS MRI: semi-quantitative intensity curves vs visual assessment of enhancement.
Salinas-Miranda E, Nanapragasam A, Nasri F, Sagheb S, Atri M.
AI summary — for doctors, generated from the abstract only
Adding semi-quantitative enhancement curves to O-RADS MRI raised specificity for malignant adnexal masses without losing much sensitivity.
What was studied
Retrospective two-center MRI review of 341 adnexal masses in 321 women who had surgery, comparing visual assessment of enhancement with a semi-quantitative intensity curve (SIC) in O-RADS MRI scoring.
Key findings
- Standard visual assessment had sensitivity 0.95, specificity 0.84 and PPV 0.77.
- With SIC, specificity rose to 0.92 and PPV to 0.90.
- Benign lesions operated on by gynecologic oncologists would have fallen from 16% to 9%.
- Inter- and intra-reader agreement was moderate to high (kappa 0.63-0.85).
Clinical relevance
Better specificity could reduce unnecessary referral of benign masses to gynecologic oncology while keeping high detection of malignancy.
Who should care
Limitations
Retrospective design at two referral centers over 2008-2018 with only two readers, so findings need prospective external validation.
Radiologists using O-RADS MRI could add a simple enhancement-curve assessment for solid-component masses to sharpen surgical triage.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
O-RADS MRI improves differentiation of benign adnexal lesions managed by a gynecologist and malignant lesions managed by a gynecologic oncologist.
Objective
To compare O-RADS MRI visual assessment (VA) of mass enhancement with a semi-quantitative intensity curve of enhancement (SIC), a modified manual version of the time intensity curve.
Materials and methods
We conducted a retrospective MRI review of patients who underwent surgical resection between January 2008 and December 2018 at two referral centers. O-RADS MRI score was assigned by two radiologists with different experience. O-RADS score ≥ 4 was considered suspicious for malignancy. Per-lesion diagnostic accuracy and impact on surgical management were compared between VA and SIC in lesions with solid components. Inter/intra-reader agreement was assessed. Borderline and invasive lesions were considered malignant.
Results
Three hundred forty-one adnexal masses in 321 women (ages 18-89) were assessed; 35.2% (120/341) of the masses were malignant. Ninety-six and two-tenths percent had some solid component (329/341). O-RADS MRI using standard VA had a sensitivity, specificity, and PPV of 0.95 (CI: 0.89-0.98), 0.84 (CI: 0.79-0.89), and 0.77 (CI:0.69-0.83), respectively. Use of SIC improved specificity (0.92 [CI: 0.88-0.95, p = 0.007) and PPV (0.90 [CI: 0.83-0.95], p = 0.002). Including SIC in O-RADS MRI would have reduced the proportion of benign lesions operated by gyneoncologists from 16% [54/341] to 9% [1/341] (p = 0.0001). O-RADS score, SIC, and VA assessment had a moderate to high inter- and intra-reader agreement (Kappa 0.63-0.85).
Conclusion
O-RADS MRI exhibited high diagnostic performance in triage of adnexal masses. Incorporation of SIC increased the specificity and PPV of O-RADS MRI with high inter- and intra-reader agreement of SIC interpretation.
Key points
Question Differentiation of benign from malignant adnexal masses is important for surgical triage. Findings Integration of semiquantitative intensity curves (SIC) into O-RADS MRI improved specificity when compared to conventional VA. Clinical relevance Integration of SIC into O-RADS MRI improves surgical triage of patients.
DOI
10.1007/s00330-026-12742-x
PMID
42668304
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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