English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The accurate classification of lymphoma subtypes using hematoxylin and eosin(H&E)-stained tissue is complicated by the wide range of morphological featuresthese cancers can exhibit. We present LymphoML - an interpretable machinelearning method that identifies morphologic features that correlate withlymphoma subtypes. Our method applies steps to process H&E-stained tissuemicroarray cores, segment nuclei and cells, compute features encompassingmorphology, texture, and architecture, and train gradient-boosted models tomake diagnostic predictions. LymphoML's interpretable models, developed on alimited volume of H&E-stained tissue, achieve non-inferior diagnostic accuracyto pathologists using whole-slide images and outperform black box deep-learningon a dataset of 670 cases from Guatemala spanning 8 lymphoma subtypes. UsingSHapley Additive exPlanation (SHAP) analysis, we assess the impact of eachfeature on model prediction and find that nuclear shape features are mostdiscriminative for DLBCL (F1-score: 78.7%) and classical Hodgkin lymphoma(F1-score: 74.5%). Finally, we provide the first demonstration that a modelcombining features from H&E-stained tissue with features from a standardizedpanel of 6 immunostains results in a similar diagnostic accuracy (85.3%) to a46-stain panel (86.1%).

LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype