Society for Immunotherapy of Cancer: Standards for Reporting of Multiplex Immunohistochemistry/Immunofluorescence Assays (STORMI)
Bibliographic record
Abstract
Multiplex immunofluorescence and immunohistochemistry (mIF/IHC) are increasingly employed antibody-based technologies that use tissue sparingly and facilitate the detection of co-localized or neighboring biomarkers. Specifically, these platforms enable spatial analyses of the tumor microenvironment as well as extended applications, for example, describing normal tissue anatomy, autoimmunity, infectious diseases, etc. mIF/IHC has greatly enhanced biomarker discovery efforts, and a growing number of studies suggest superiority to traditional IHC. Standardization of staining approaches, reporting of image analysis strategies and resultant data is critical for facilitating cross-study comparisons, validation, deployment, and generalization of findings. To address this challenge, The Society for Immunotherapy of Cancer (SITC) previously published two articles providing best practice guidelines for mIF/IHC staining, image analysis, and data sharing. Here, SITC convened stakeholders to develop the third article in the series, a consensus checklist for scientific reporting of mIF/IHC data to support and complement the best practice guidelines. The checklist includes critical components of mIF/IHC applications to be defined within publications such as detailed descriptions of analytical validation; image acquisition, selection, and registration methods; and cell clustering and spatial analysis strategies, amongst others. Such information will help with data reproducibility and comparison across studies towards future drug and assay development.
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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 | 0.277 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.017 |
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".