Diagnostic Algorithm to Differentiate Benign Atypical Leiomyomas from Malignant Uterine Sarcomas with Diffusion-weighted MRI
Bibliographic record
Abstract
Background Improving the differentiation of uterine sarcomas from atypical leiomyomas remains a clinical challenge and is needed to avoid inappropriate surgery. Purpose To develop a diagnostic algorithm including diffusion-weighted MRI criteria to differentiate malignant uterine sarcomas from benign atypical leiomyomas. Materials and Methods This case-control retrospective study identified women with an atypical uterine mass at MRI between January 2000 and April 2017, with surgery or MRI follow-up after 1 year or longer. A diagnostic algorithm including T2-weighted MRI and diffusion-weighted imaging (DWI) signal and apparent diffusion coefficient (ADC) values was developed to predict for sarcoma. The training set consisted of 51 sarcomas and 105 leiomyomas. Two external validation sets were used to evaluate interreader reproducibility (16 sarcomas; 26 leiomyomas) and impact of reader experience (29 sarcomas; 30 leiomyomas). Wilson confidence intervals (CIs) were calculated for sensitivity and specificity. Results Evaluated were 156 women (median age, 50 years; interquartile range, 44–63 years). Predictive MRI criteria for malignancy were enlarged lymph nodes or peritoneal implants, high DWI signal greater than that in endometrium, and ADC less than or equal to 0.905 × 10−3 mm2/sec. Conversely, a global or focal area of low T2 signal intensity and a low or an intermediate DWI signal less than that in endometrium or lymph nodes allowed readers to confidently diagnose as benign a uterine mass demonstrating one or more of these signs (P < .001) in 100% cases in all three data sets. The sensitivities and specificities of the algorithm for diagnosis of malignancy were 98% (50 of 51 masses; 95% CI: 90%, 100%) and 94% (99 of 105 masses; 95% CI: 88%, 98%) in the training set; 88% (14 of 16 masses; 95% CI: 64%, 97%) and 100% (26 of 26 masses; 95% CI: 87%, 100%) in the validation set; and 83% (24 of 29 masses; 95% CI: 65%, 92%) and 97% (29 of 30 masses; 95% CI: 83%, 99%) for the less experienced reader, respectively. Conclusion A diagnostic algorithm with predictive features including lymphadenopathy, high diffusion-weighted imaging signal with reference to endometrium, and low apparent diffusion coefficient enabled differentiation of malignant sarcomas from atypical leiomyomas, and it may assist inexperienced readers. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Méndez in this issue.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".