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Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

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Abstract

Purpose

Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the same time, NSCLC management through KRAS and EGFR mutation profiling faces challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC patients based on radiomic features from low-dose computed tomography (CT), contrast-enhanced diagnostic quality CT (CTD), and positron emission tomography (PET) imaging modalities and use of machine learning algorithms.

Methods

Our study involved NSCLC patients including 150 PET, low-dose CT, and CTD images. Radiomic features from original and preprocessed (including 64 bin discretizing, Laplacian-of-Gaussian (LOG), and Wavelet) images were extracted. Conventional clinically used standard uptake value (SUV) parameters and metabolic tumor volume (MTV) were also obtained from PET images. Highly correlated features were pre-eliminated, and false discovery rate (FDR) correction was performed with the resulting q-values reported for univariate analysis. Six feature selection methods and 12 classifiers were then used for multivariate prediction of gene mutation status (provided by polymerase chain reaction (PCR)) in patients. We performed 10-fold cross-validation for model tuning to improve robustness, and our developed models were assessed on an independent validation set with 68 patients (common in all three imaging modalities). The average area under the receiver operator characteristic curve (AUC) was utilized for performance evaluation.

Results

The best predictive power for conventional PET parameters was achieved by SUVpeak (AUC 0.69, p value = 0.0002) and MTV (AUC 0.55, p value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved AUC performance to 0.75 (q-value 0.003, Short-Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and 0.71 (q-value 0.00005, Large Dependence Low Gray-Level Emphasis feature of GLDM in LOG preprocessed image of CTD with sigma value 5) for EGFR and KRAS, respectively. Furthermore, multivariate machine learning-based AUC performances were significantly improved to 0.82 for EGFR (LOG preprocessed image of PET with sigma 3 with variance threshold (VT) feature selector and stochastic gradient descent (SGD) classifier (q-value = 4.86E-05) and 0.83 for KRAS (LOG preprocessed image of CT with sigma 3.5 with select model (SM) feature selector and SGD classifier (q-value = 2.81E−09).

Conclusion

Our work demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients and showed improved predictive power relative to conventional image-derived metrics.

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Funding

This study was funded by “Rajaie Cardiovascular Medical and Research Centre” and the Swiss National Science Foundation under Grant SNSF 320030_176052.

Author information

Authors and Affiliations

  1. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland

    Isaac Shiri, Ghasem Hajianfar, Hamid Abdollahi & Habib Zaidi

  2. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran

    Hasan Maleki & Mehrdad Oveisi

  3. Department of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran

    Hasan Maleki

  4. Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran

    Hamid Abdollahi

  5. Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA

    Saeed Ashrafinia & Arman Rahmim

  6. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA

    Saeed Ashrafinia

  7. LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France

    Mathieu Hatt

  8. Geneva University Neurocenter, Geneva University, Geneva, Switzerland

    Habib Zaidi

  9. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

    Habib Zaidi

  10. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark

    Habib Zaidi

  11. Department of Computer Science, University of British Columbia, Vancouver, BC, Canada

    Mehrdad Oveisi

  12. Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada

    Arman Rahmim

  13. Department of Integrative Oncology, BC Cancer Research Centre, 675 West 10th Ave, Vancouver, BC, V5Z 1L3, Canada

    Arman Rahmim

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  1. Isaac Shiri
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Correspondence to Arman Rahmim.

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Shiri, I., Maleki, H., Hajianfar, G. et al. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms. Mol Imaging Biol 22, 1132–1148 (2020). https://doi.org/10.1007/s11307-020-01487-8

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