121 Quantifying statistical and systematic uncertainties in predicting clinical outcomes using multiplex immunofluorescence
Notice bibliographique
Résumé
<h3>Background</h3> Statistical analysis of multiplex immunofluorescence images can be used to predict patient outcomes. Recent advances have enabled complex analyses of marker expressions across every cell on a slide, as well as their spatial correlations.<sup>1 2</sup> In parallel, calibration of the microscope slides to correct for errors in the imaging and for differences between different microscopes, staining batches, and slides, has also advanced.<sup>3</sup> One critical missing piece is the connection between the two. If after calibration there remains a 0.8% uncertainty on the flatfielding, how does that translate into an uncertainty on the prediction of patient response? <h3>Methods</h3> Using the AstroPath platform, we recently that CD8+FoxP3+ cells can be used to predict response to treatment. By examining the neighbors of CD8+FoxP3+ cells, we identified ‘CD8+FoxP3+-like’ neighborhoods that show similar predictive power but are much more abundant than the cells themselves.<sup>2</sup> Here, we reran this analysis, estimating several sources of statistical and systematic uncertainty and their application to the final result. Statistical uncertainty on the cell count was estimated using a Poisson distribution. The effects of uncertainties in the scanning, processing, segmentation, and phenotyping calibration were estimated by examining cells in different parts of the high-powered field, and in particular by using the 20% overlap between adjacent high-power fields <h3>Results</h3> The remaining systematic uncertainties from image processing, after applying flatfielding and other corrections, are negligible. We find them, conservatively, to be less than 0.3%. These uncertainties would likely be higher for an analysis that relied more heavily on marker expression levels. Statistical uncertainties are more significant. The Poisson uncertainty on the count of relevant cells is especially important for rare phenotypes, such as actual CD8+FoxP3+ cells, where it is 25% or higher for many samples. Although we find the area under the receiving operator characteristic curve (AUC) is 0.79, the Poisson uncertainty gives (0.74, 0.83) at 68% confidence level (CL) and (0.66, 0.86) at 95% CL. For CD8+FoxP3+-like neighborhoods, which are more abundant, the Poisson error is 2–3% for most samples. The nominal AUC is 0.80, with a much smaller uncertainty: (0.79, 0.80) at 68% CL and (0.76, 0.81) at 95% CL. <h3>Conclusions</h3> A quantitative approach to statistical and systematic uncertainties is shown in application to multiplex immunofluorescence analysis. Accounting for these uncertainties is not only necessary to properly understand the result, but also identifies which calibrations have the greatest effect on the analysis and should therefore be prioritized for further improvement. <h3>References</h3> Berry S, Giraldo NA, Green BF, <i>et al.</i> Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. <i>Science</i>. 2021;<b>372</b>(6547). Cottrell TR, Cohen EI, Roskes JS, <i>et al.</i> Early, effector CD8+FoxP3+ cells and their topology associate with outcomes in patients with non-small cell lung carcinoma (NSCLC) receiving neoadjuvant anti-PD-1-based therapy. submitted to <i>Nat Cancer</i>. 2023. Eminizer M, Nagy M, Engle EL, <i>et al</i>. Comparing and correcting spectral sensitivities between multispectral microscopes: A prerequisite to clinical implementation. <i>Cancers</i>. 2023;<b>15</b>(12):3109
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».