Cholesteatoma: multishot echo-planar <i>vs</i> non echo-planar diffusion-weighted MRI for the prediction of middle ear and mastoid cholesteatoma
Bibliographic record
Abstract
Objective: We aimed to compare a newer readout-segmented echoplanar imaging (RS-EPI) technique with the established single shot turbo spin echo (SS-TSE) non-EPI diffusion-weighted imaging (DWI) in detecting surgically validated cholesteatoma. Methods: We retrospectively reviewed 358 consecutive MRI studies in 285 patients in which both RS-EPI and non-EPI DWI sequences were performed. Each diffusion sequence was reviewed independently and scored negative, indeterminate or positive for cholesteatoma in isolation and after reviewing the T 1 W sequence. Average artefacts scores were evaluated and the lesion size measured as a distortion indicator. The imaging scores were correlated with surgical validation, clinical and imaging follow-up. Results: There were 239 middle ear and central mastoid tract and 34 peripheral mastoid lesions. 102 tympanomastoid operations were performed. The positive predictive value ( PPV), post-operative PPV, primary PPV, negative predictive value were 93%, 95%, 87.5%, 70% for RS-EPI and 92.5%, 93.6%, 90%, 79% for non-EPI DWI. There was good agreement between the two techniques (k = 0.75). Non-EPI DWI is less susceptible to skull base artefacts although the mean cholesteatoma measurement difference was only 0.53 mm. Conclusion: RS-EPI has comparable PPV with non-EPI DWI in both primary and post-operative cholesteatoma but slightly lower negative predictive value. When there is a mismatch, non-EPI DWI better predicts the presence of cholesteatoma. There is good agreement between the sequences for cholesteatoma diagnosis. The T 1 W sequence is very important in downgrading indeterminate DWI signal lesions to a negative score. Advances in knowledge: This is, to our knowledge, the first study to compare a multishot EPI DWI technique with the established non- EPI DWI in cholesteatoma diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".