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Radiogenomics: Lung Cancer-Related Genes Mutation Status Prediction

  • Catarina DiasEmail author
  • Gil Pinheiro
  • António Cunha
  • Hélder P. Oliveira
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)

Abstract

Advances in genomics have driven to the recognition that tumours are populated by different minor subclones of malignant cells that control the way the tumour progresses. However, the spatial and temporal genomic heterogeneity of tumours has been a hurdle in clinical oncology. This is mainly because the standard methodology for genomic analysis is the biopsy, that besides being an invasive technique, it does not capture the entire tumour spatial state in a single exam. Radiographic medical imaging opens new opportunities for genomic analysis by providing full state visualisation of a tumour at a macroscopic level, in a non-invasive way. Having in mind that mutational testing of EGFR and KRAS is a routine in lung cancer treatment, it was studied whether clinical and imaging data are valuable for predicting EGFR and KRAS mutations in a cohort of NSCLC patients. A reliable predictive model was found for EGFR (AUC = 0.96) using both a Multi-layer Perceptron model and a Random Forest model but not for KRAS (AUC = 0.56). A feature importance analysis using Random Forest reported that the presence of emphysema and lung parenchymal features have the highest correlation with EGFR mutation status. This study opens new opportunities for radiogenomics on predicting molecular properties in a more readily available and non-invasive way.

Keywords

Radiogenomics Mutation status Predictive models 
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Notes

Acknowledgments

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Catarina Dias
    • 1
    • 2
    Email author
  • Gil Pinheiro
    • 1
  • António Cunha
    • 1
    • 3
  • Hélder P. Oliveira
    • 1
    • 4
  1. 1.Instituto de Engenharia de Sistemas e Computadores, Tecnologia e CiênciaPortoPortugal
  2. 2.Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  3. 3.Universidade de Trás-os-Montes e Alto DouroVila RealPortugal
  4. 4.Faculdade de CiênciasUniversidade do PortoPortoPortugal

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