Confounding factors and biases abound when predicting molecular biomarkers from histological images
Muhammad Dawood; Kim Branson; Sabine Tejpar; Nasir Rajpoot; Fayyaz ul Amir Afsar Minhas · 2026 · Nature Biomedical Engineering
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Deep learning models that infer clinically relevant biomarker status from tissue images are being explored as rapid and low-cost alternatives to molecular testing. Here we show, through statistical analysis across multiple cancer types, datasets and modelling approaches, that the datasets used to train these models contain strong dependencies between biomarkers and clinicopathological features, which prevent models from isolating the effect of a single biomarker and lead them to learn confounded signals. Consequently, their prediction accuracy varies substantially with the status of codependen
Abstract by Muhammad Dawood; Kim Branson; Sabine Tejpar; Nasir Rajpoot; Fayyaz ul Amir Afsar Minhas, Nature Biomedical Engineering (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41551-026-01616-8