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Record W4220680613 · doi:10.1101/2022.03.10.22272226

A Pilot Validation Study Comparing FIBI, a Slide-Free Imaging Method, with Standard FFPE H&E Tissue Section Histology for Primary Surgical Pathology Diagnosis

2022· preprint· en· W4220680613 on OpenAlexaff
Alexander D. Borowsky, Richard M. Levenson, Allen M. Gown, Taryn Morningstar, Thomas A. Fleury, Gregory C. Henderson, Kurt B. Schaberg, Amelia Sybenga, Eric F. Glassy, Sandra L. Taylor, Farzad Fereidouni

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesNational Institutes of Health
KeywordsDigital pathologySurgical pathologyMedical diagnosisMedicinePathologyNuclear medicineFixation (population genetics)HistologyBiomedical engineering

Abstract

fetched live from OpenAlex

Abstract Introduction Digital pathology whole slide images (WSI) have been recently approved by the FDA for primary diagnosis in clinical surgical pathology practices. These WSI are generated by digitally scanning standard formalin-fixed and paraffin-embedded (FFPE) H&E-stained tissue sections mounted on glass microscope slides. Novel imaging methods are being developed that can capture the surface of tissue without requiring prior fixation, paraffin embedding, or tissue sectioning. One of these methods, FIBI (Fluorescence Imitating Brightfield Imaging), an optically simple and low-cost technique, was developed by our team and used in this study. Methods 100 de-identified surgical pathology samples were obtained from the UC Davis Health Pathology Laboratory. Samples were first digitally imaged by FIBI, and then embedded in paraffin, sectioned at 4 µm, mounted on glass slides, H&E stained, and scanned using the Aperio/Leica AT2 scanner. The resulting digital images from both FIBI and H&E scan sets were uploaded to PathPresenter and viewed in random order and modality (FIBI or H&E) by each of 4 reading pathologists. After a 30-day washout, the same 100 cases, in random order, were presented in the alternate modality to what was first shown, to the same 4 reading pathologists. The data set consisted, therefore, of 100 reference diagnoses and 800 study pathologist reads (400 FIBI and 400 H&E). Each study read was compared to the reference diagnosis for that case, and also compared to that reader’s diagnosis across both modalities for each case. Categories of concordance, minor and major discordance were adjudicated by the study team based on established criteria. Results The combined category, concordance or minor discordance, was scored as “no major discordance.” The overall agreement rate (compared to the reference diagnosis), across 800 reads, was 97.9%. This consisted of 400 FIBI reads at 97.0% vs. reference and 400 H&E reads vs. reference at 98.8%. Minor discordances (defined as alternative diagnoses without clinical treatment or outcome implications) were 6.1% overall, 7.2% for FIBI and 5.0% for HE. Conclusions Pathologists without specific experience or training in FIBI imaging interpretation can provide accurate diagnosis from FIBI slide-free images. Concordance/discordance rates are similar to published rates for comparisons of WSI to standard light microscopy of glass slides for primary diagnosis that led to FDA approval. The present study was more limited in scope but suggests that a follow-on formal clinical trial is feasible. It may be possible, therefore, to develop a slide-free, non-destructive approach for primary pathology diagnosis. Such a method promises improved speed, reduced cost, and better conservation of tissue for advanced ancillary studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.053
GPT teacher head0.336
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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