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Paper

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

New research · Pathology

Cancer research · 2mo

AI / informaticsCancer research· Jul 2026

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

Zhenya Zang, David A Dorward, Sophie Ihuoma … Qiang Wang

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Molecular PathologyAI / informaticsQ1IF 11.2

Label-free deep learning imaging predicts epidermal growth factor receptor mutation status in lung adenocarcinoma

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

Zhenya Zang … Qiang Wang

Cancer research · Jul 2026

Background

UNLABELLED: Accurate prediction of epidermal growth factor receptor (epidermal growth factor receptor) mutations is essential for guiding targeted therapy in non-small cell lung cancer (non-small cell lung cancer) and is routinely performed in clinical pathology samples.

Results

0.966

AUROC of 0.966 for detecting epidermal growth factor receptor mutations, 1.0 is perfect

More results

However, these methods still depend on conventional tissue staining.

This strategy also provided accurate classification of the two most common non-small cell lung cancer epidermal growth factor receptor mutations of exon 19 deletion and exon 21 point mutation across the dataset.

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Conclusion

SIGNIFICANCE: A label-free fluorescence lifetime imaging-based deep learning method accurately predicts epidermal growth factor receptor mutations in lung adenocarcinomas from unstained, formalin-fixed tissue, enabling rapid pathway development and accelerating targeted therapies for patients.

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