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

Shiri Lord, Isaac; Maleki, Hasan; Hajianfar, Ghasem; Abdollahi, Hamid; Ashrafinia, Saeed; Hatt, Mathieu; Zaidi, Habib; Oveisi, Mehrdad; Rahmim, Arman

doi:10.1007/s11307-020-01487-8

Scientific article

English

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

ContributorsShiri Lord, Isaac

; Maleki, Hasan; Hajianfar, Ghasem; Abdollahi, Hamid; Ashrafinia, Saeed; Hatt, Mathieu; Zaidi, Habib

; Oveisi, Mehrdad; Rahmim, Arman

Published inMolecular Imaging and Biology, vol. 22, no. 4, p. 1132-1148

Publication date2020

Abstract

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.

Keywords

EGFR
KRAS
Machine learning
NSCLC
PET/CT
Radiogenomics

Affiliation

Research group

Imagerie Médicale (TEP et TEMP) (542)

Funding

Swiss National Science Foundation - Towards patient-specific hybrid whole-body PET parametric imaging [320030_176052]

Citation (ISO format)

SHIRI LORD, Isaac et al. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms. In: Molecular Imaging and Biology, 2020, vol. 22, n° 4, p. 1132–1148. doi: 10.1007/s11307-020-01487-8

Article (Published version)

Identifiers

PID : unige:136991
DOI : 10.1007/s11307-020-01487-8
PMID : 32185618

Commercial URLhttps://link.springer.com/article/10.1007%2Fs11307-020-01487-8

ISSN of the journal1536-1632

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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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