Diagnostic Performance of International Ovarian Tumor Analysis Logistic Regression Model LR2 for Adnexal Masses Classification at a Tertiary Gynecology Center in Singapore
Bibliographic record
Abstract
Background: The International Ovarian Tumor Analysis (IOTA) LR2 model has been shown to provide a reasonably accurate preoperative classification of ovarian tumors. The purpose of this study was to evaluate the diagnostic performance of the IOTA LR2 model in distinguishing benign and malignant adnexal masses in the Singapore population. Methods: This was a retrospective study in a tertiary referral center. Women who attended the Gynecology Unit at Singapore General Hospital with evidence of adnexal tumor on ultrasound examination were evaluated using the IOTA LR2 protocol. The LR2 model was then used to calculate the probability of malignancy. Likelihood ratio of malignancy greater than 10% classifies the mass as malignant. The preoperative diagnosis of women who underwent surgery within 120 days of ultrasound examination was correlated with the final histopathological result. Results: Of the 353 women included in the final study population, 223 had benign disease, 29 had borderline malignant, and 101 had invasive cancer. The IOTA LR2 model had a sensitivity of 79.2% (95% confidence interval (CI), 71.2-85.8%) and a specificity of 79.4% (95% CI, 73.5-84.5%). The area under the receiver-operating characteristics curve was 0.84 (95% CI, 0.80 - 0.89). Conclusions: The IOTA LR2 model maintained its overall diagnostic accuracy when used in our local population. Although it is useful as a first-step test for triaging women with ovarian masses for surgery, a second-stage test would be required to minimize the number of women with benign disease being offered surgery for suspected ovarian malignancy. J Clin Gynecol Obstet. 2021;10(3):67-72 doi: https://doi.org/10.14740/jcgo758
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".