Practical Tips for Reporting Adnexal Lesions Using O-RADS MRI
Bibliographic record
Abstract
The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI risk stratification system provides a standardized lexicon and evidence-based risk score for evaluation of adnexal lesions. The goals of the lexicon and risk score are to improve report quality and communication between radiologists and clinicians, reduce variability in the reporting language, and optimize management of adnexal lesions. The O-RADS MRI risk score is based on the presence or absence of specific imaging features, including the lipid content, enhancing solid tissue, number of loculi, and fluid type. The probability of malignancy ranges from less than 0.5% when there are benign features to approximately 90% when there is solid tissue with a high-risk time-intensity curve. This information can aid in optimizing management of patients with adnexal lesions. The authors present an algorithmic approach to the O-RADS MRI risk stratification system and highlight key teaching points and common pitfalls. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.042 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".