Performance Assessment of a Deep Learning–based Algorithm for Ovarian Cancer Histotyping in an Independent Data Set
Bibliographic record
Abstract
Artificial intelligence diagnostic tools show promise for improving histotype classification in epithelial ovarian cancer but face challenges due to slide variability across institutions. To address this domain shift, the adversarial Fourier-based domain adaptation (AIDA) model was developed. This retrospective study evaluates AIDA's performance in classifying the 5 major ovarian cancer subtypes using an independent cohort. Surgically treated patients diagnosed with clear cell (CCC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC), or mucinous (MC) ovarian cancer at Amsterdam University Medical Center (1985-2022) were included in the study. The deep learning method AIDA, trained on data from Vancouver General Hospital, was applied to all cases. Final histotype predictions were made through majority voting across 15 independently trained models. For misclassified cases, up to 3 additional slides were scanned, and the AIDA model was retrained. Classification was then assessed using single-slide and majority voting approaches. The AIDA algorithm achieved an overall balanced accuracy of 79.7% across all histotypes. Accuracy was highest for CCC (90.9%) and LGSC (89.8%), and lowest for EC (62.4%). Common misclassifications included MC as EC and EC as HGSC or LGSC. Retraining with additional slides improved balanced accuracy to 85.8% based on single-slide voting and 82.6% based on majority voting. This study highlights the future potential of the AIDA model in classifying epithelial ovarian cancer histotypes. With further refinement to improve performance on more challenging cases, the model could enhance diagnostic accuracy in clinical practice.
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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 | 0.006 | 0.008 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".