Comparison of O-RADS 2022 and Simple Rules Ultrasound Classifications to Predict Adnexal Malignancy
Bibliographic record
Abstract
This study compares performance of O-RADS (version 2022) and Simple Rules (SR) ultrasound criteria in a cohort of asymptomatic pathology-proven adnexal masses and evaluates O-RADS and SR inter-observer agreement in a subset of patients. Retrospective Cohort Study. Diagnostic imaging. Consecutive women who underwent surgical resection of adnexal mass(es) between January 2008 and December 2018 at two University Hospitals, a time period when cine clips were available for all ultrasounds. One experienced radiologist, blinded to pathological diagnosis, categorized all imaging by O-RADS and SR criteria. 791 adnexal masses in 762 patients were assessed, aged 18-92 (44 ± 15); 628 benign, 49 LMP, 114 malignant. O-RADS categories were 2 (n=309), 3 (n=165), 4 (n=181), 5 (n=136) with malignant rates of 0.3%, 3%, 25%, and 82% respectively. Application of simple rules criteria identified 561 masses as benign and 230 as malignant. Combining O-RADS 4 and 5 categories as being malignant, sensitivity, specificity, NPV, PPV, and accuracy to detect invasive/LMP masses were 96% (CI:92-99%), 75% (CI:71-78%), 99% (CI:97-100%), 49% (CI:44-55%), and 79% (CI:76-82%). Corresponding results for SR were 96% (CI:91-98%), 89% (CI:85-91%), 99% (CI:98-100%), 68% (CI:61-74%), and 90% (CI:87-92%) with specificity, PPV, accuracy of SR being statistically significantly higher than O-RADs (p<0.0001). AUC-ROC of SR and O-RADS were 0.920 and 0.855 (p=0.01). Inter-observer agreement between the three readers for review of a subset of 172 masses were 0.89, 0.91, and 0.93 for SR benign vs malignant and 0.71, 0.75, and 0.75 for O-RADS (2/3) vs O-RADS 4/5. Adnexal mass assessment with SR performs significantly better than US O-RADS classification in specificity, PPV, and accuracy. Risk stratification by experienced radiologists using SR criteria outperforms O-RADS and can result in better triage to surgical gynecologists and oncologists with an improved rate of predicting malignancy.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".