Recommendations for Validating Whole Slide Imaging in Pathology: College of American Pathologists Pathology and Laboratory Quality Center
Bibliographic record
Abstract
Whole slide imaging (WSI) is increasingly being used for diagnostic purposes, education, and research. Concern has arisen whether WSI can replace the conventional light microscope as the method by which pathologists view patient samples and render a diagnosis (primary and/or consultation). Validation of WSI is important to ensure that digitized slides are at least equivalent to glass slides. There are currently no standardized guidelines regarding validation of WSI for clinical diagnostic use. The CAP Pathology and Laboratory Quality Center convened a nonvendor panel from North America with expertise in digital pathology. Data sources for recommendations were derived from panel consensus and an extensive literature review. For the final selected articles, publication year, author country, WSI clinical application, number of persons and cases used, validation method, and outcome measurement were recorded, analyzed, and graded. Validation of WSI is necessary to ensure that a pathologist using this technique to view digitized glass slides can consistently make the same clinical interpretation as from viewing the glass slides using a traditional bright-field microscope. Validation should address technical and interpretation components, thus demonstrating that the WSI system is capable of producing an acceptable digital slide for interpretation. Approved use of WSI should be limited to the conditions under which validation occurred. A minimum sample size is recommended for a validation study that includes a broad spectrum of specimen types and diagnoses likely to be encountered for the intended clinical use, thereby simulating real-world clinical practice. A pathologist must be involved in the validation process. Measurable outcomes should document diagnostic concordance (accuracy) between digitized and glass slides for the same observer (intraobserver variability) within a reasonable washout period. Our intention is to submit detailed recommendations for public commentary before final publication.
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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 | 0.233 | 0.495 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.020 | 0.009 |
| Research integrity | 0.025 | 0.029 |
| Insufficient payload (model declined to judge) | 0.014 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".