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Record W4313910225 · doi:10.1097/pai.0000000000001095

Reinventing Nuclear Histo-score Utilizing Inherent Morphologic Cutoffs: Blue-brown Color H-score (BBC-HS)

2023· article· en· W4313910225 on OpenAlexaff
Phillipe Price, Usharani Ganugapati, Zoran Gatalica, Archan Kakadekar, James Macpherson, Louise Quenneville, Henrike Rees, Elzbieta Slodkowska, Janarthanee Suresh, Darryl Yu, Hyun J. Lim, Emina Torlakovic

Bibliographic record

VenueApplied immunohistochemistry & molecular morphology · 2023
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreUniversity of OttawaSaskatchewan Health AuthorityUniversity of Saskatchewan
Fundersnot available
KeywordsStainStainingImmunohistochemistryH&E stainCutoffIntraclass correlationPathologyMedicineKappaNuclear medicineInternal medicineMathematicsPhysicsClinical psychology

Abstract

fetched live from OpenAlex

Immunohistochemistry (IHC) is a testing methodology that is widely used for large number of diagnostic, prognostic, and predictive biomarkers. Although IHC is a qualitative methodology, in addition to threshold-based stratification (positive vs. negative), the increasing levels of expression of some of these biomarkers often lead to more intense staining, which published evidence linked to specific diagnosis, prognosis, and responses to therapy. It is essential that the descriptive thresholds between positive and negative staining, as well as between frequently used graded categories of staining intensity (eg, 1+, 2+, 3+) are standardized and reproducible. Histo-score (H-score) is a frequently used scoring system that utilizes these categories. Our study introduces categorization of the cutoff points between positive and negative results and graded categories of staining intensity for nuclear IHC biomarker assays based on color interaction between hematoxylin and diaminobenzidine (DAB); the Blue-brown Color H-score (BBC-HS). Six cases of diffuse large B-cell lymphoma were stained for a nuclear marker MUM1. The staining was assessed by H-score by 12 readers. Short tutorial and illustrated instructions were provided to readers. The novel scoring system in this study uses the interaction between DAB (DAB, brown stain) and hematoxylin (blue counterstain) to set thresholds between "0" (negative nuclei), "1+" (weakly positive nuclei), "2+" (moderately positive nuclei), and "3+" (strongly positive nuclei). The readers recorded scores for 300 cells. Krippendorff alpha (K-alpha) and intraclass correlation coefficient (ICC) were calculated. We have also assessed if reliability improved when counting the first 100 cells, first 200 cells, and for the total 300 cells using K-alpha and ICC. To assess the performance of each individual reader, the mean H-score and percent positive score (PPS) for each case was calculated, and the bias was calculated between each reader's score and the mean. K-alpha was 0.86 for H-score and 0.76 for PPS. ICC was 0.96 for H-score and 0.92 for PPS. The biases for H-score ranged from -58 to 41, whereas for PPS it ranged from -27% to 33%. Overall, most readers showed very low bias. Two readers were consistently underscoring and 2 were consistently overscoring compared with the mean. For nuclear IHC biomarker assays, our newly proposed cutoffs provide highly reliable/reproducible results between readers for positive and negative results and graded categories of staining intensity using existing morphologic parameters. BBC-HS is easy to teach and is applicable to both human eye and image analysis. BBC-HS application should facilitate the development of new reliable/reproducible scoring schemes for IHC biomarkers.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2023
Admission routes1
Has abstractyes

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