First published 10/3/2026, 1:02:15 AM · Last updated 10/3/2026, 1:02:15 AM

P2.06.65 An Ensemble Approach to Predict Three-Class PD-L1 Expression From H&E Whole-Slide Images

A journal article reports a machine-learning ensemble to predict three-class PD-L1 expression using H&E-stained whole-slide images. The source is published by Elsevier BV.

Illustrative image: The Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods, part of the Urban Spatial Data collection, models urban settlements as of 2015. The input data for these models were two types of satellite imagery: Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) data from the Day/Night Band (DNB), and Moderate Resolution Imaging Spectroradiometer (MODIS) corrected daytime Normalized Difference Vegetation Index (NDVI). After several machine le — SEDACMaps/Wikimedia Commons, CC BY 2.0

Categories: science-and-space, technology, public-health

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 3/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 2/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 3/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 4/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The study addresses automated PD-L1 classification from routine pathology images, which is relevant to diagnostic research.

Sources

P2.06.65 An Ensemble Approach to Predict Three-Class PD-L1 Expression From H&E Whole-Slide Images

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