A Pilot Validation Study Comparing Fluorescence-Imitating Brightfield Imaging, A Slide-Free Imaging Method, With Standard Formalin-Fixed, Paraffin-Embedded Hematoxylin-Eosin–Stained Tissue Section Histology for Primary Surgical Pathology Diagnosis
Bibliographic record
Abstract
CONTEXT.—: Digital pathology using whole slide images has been recently approved to support primary diagnosis in clinical surgical pathology practices. Here we describe a novel imaging method, fluorescence-imitating brightfield imaging, that can capture the surface of fresh tissue without requiring prior fixation, paraffin embedding, tissue sectioning, or staining. OBJECTIVE.—: To compare the ability of pathologists to evaluate direct-to-digital images with standard pathology preparations. DESIGN.—: One hundred surgical pathology samples were obtained. Samples were first digitally imaged, then processed for standard histologic examination on 4-μm hematoxylin-eosin-stained sections and digitally scanned. The resulting digital images from both digital and standard scan sets were viewed by each of 4 reading pathologists. The data set consisted of 100 reference diagnoses and 800 study pathologist reads. Each study read was compared to the reference diagnosis, and also compared to that reader's diagnosis across both modalities. RESULTS.—: The overall agreement rate, across 800 reads, was 97.9%. This consisted of 400 digital reads at 97.0% versus reference and 400 standard reads versus reference at 98.8%. Minor discordances (defined as alternative diagnoses without clinical treatment or outcome implications) were 6.1% overall, 7.2% for digital, and 5.0% for standard. CONCLUSIONS.—: Pathologists can provide accurate diagnoses from fluorescence-imitating brightfield imaging slide-free images. Concordance and discordance rates are similar to published rates for comparisons of whole slide imaging to standard light microscopy of glass slides for primary diagnosis. It may be possible, therefore, to develop a slide-free, nondestructive approach for primary pathology diagnosis.
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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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".