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  3. Genomic Characterization Of Lung Cancer In Never-smokers Using Deep Learning.
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  3. Genomic Characterization Of Lung Cancer In Never-smokers Using Deep Learning.

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Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.

Monjoy Saha1, Thi-Van-Trinh Tran1, Praphulla M S Bhawsar1

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.

Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc
|February 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel deep learning model for predicting genetic features in never-smoker lung adenocarcinomas (NS-LUAD) from histology images. The model accurately identifies 11 molecular alterations, aiding in personalized treatment for this distinct lung cancer subset.

Keywords:
artificial intelligencedeep learningdriver geneslung adenocarcinomamutational signaturesnever smokers

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Area of Science:

  • Computational pathology
  • Oncology
  • Bioinformatics

Background:

  • Deep learning shows promise for inferring genetic features from whole-slide images (WSIs).
  • Lung adenocarcinomas in never-smokers (NS-LUAD) are molecularly distinct but understudied by current deep learning models.
  • Existing models often focus on smoker populations with limited molecular scope and variable performance.

Purpose of the Study:

  • To develop and validate a customized deep convolutional neural network for multilabel classification of molecular alterations in NS-LUAD from H&E-stained WSIs.
  • To enable simultaneous prediction of 16 molecular alterations, including mutations, amplifications, fusions, and copy number alterations.
  • To optimize the model for reduced computational complexity while maintaining predictive accuracy.

Main Methods:

  • A ResNet50-based deep convolutional neural network with architectural modifications (simplified residual blocks, selective shortcuts, sigmoid classification head) was developed.
  • The model was trained and evaluated on 495 WSIs from the Sherlock-Lung study using a 70% training, 10% internal test, and 30% held-out validation set split.
  • Performance was assessed using area under the receiver operating characteristic curve (AUROC) for predicting 16 molecular features.

Main Results:

  • The model achieved high AUROC values (0.84-0.93) for 11 features, including EGFR, KRAS, TP53 mutations, and ALK fusions.
  • Performance was moderate for tumor mutational burden (AUROC=0.67) and KRAS hotspot mutations (e.g., p.G12C: AUROC=0.74).
  • The customized model outperformed established architectures like Inception-v3 for most features on the same dataset.

Conclusions:

  • The developed deep learning model demonstrates significant potential for predicting molecular alterations in NS-LUAD directly from WSIs.
  • This approach could facilitate triaging for molecular testing and inform precision treatment strategies for never-smoker lung cancer patients.
  • Further optimization may enhance its utility in clinical decision-making for NS-LUAD.