Original Investigation| Volume 30, ISSUE 6, P1039-1046, June 2023

Download started.


Radiomics for Detection of the EGFR Mutation in Liver Metastatic NSCLC

      Rationale and Objectives

      The research aims to investigate whether MRI radiomics on hepatic metastasis from primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type, and develop a prediction model based on combination of primary tumor and the metastasis.

      Materials and Methods

      A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used to extract radiomics features from intra- and peritumoral areas of both primary tumor and metastasis. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify most predictive features and to develop radiomics signatures (RSs) for prediction of the EGFR mutation status. The receiver operating characteristic (ROC) curve analysis was performed to assess the prediction capability of the developed RSs.


      A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis, respectively. The RS-Combine by combination of the primary tumor and metastasis achieved the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant difference between EGFR mutant and wild-type groups (p < 0.05) in the training set.


      The study indicates that hepatic metastasis-based radiomics can be used to detect the EGFR mutation. The developed multiorgan combined radiomics signature may be helpful to guide individual treatment strategies for patients with metastatic NSCLC.

      Key Words


      CET1 (contrast-enhanced T1-weighted), EGFR (epidermal growth factor receptor), Gd-DTPA (gadolinium-diethylenetriamine penta-acetic acid), GLCM (gray level co-occurrence matrix), GLDM (gray level dependence matrix), GLRLM (gray level run length matrix), GLSZM (gray level size zone matrix), ICC (intraclass correlation coefficient), LA (lung adenocarcinoma), LASSO (least absolute shrinkage and selection operator), MRI (magnetic resonance imaging), NGTDM (neighboring gray tone difference matrix), NSCLC (nonsmall cell lung cancer), ROC (receiver operating characteristic), ROI (region of interest), RSs (radiomics signatures), TKIs (tyrosine kinase inhibitors)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Zheng LL
        • Sun RZ
        • Zhu YH
        • et al.
        Lung microbiome alterations in NSCLC patients.
        Sci Rep-Uk. 2021; 11: 11736
        • Sung H
        • Ferlay J
        • Siegel RL
        • et al.
        Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
        CA Cancer J Clin. 2021; 71: 209-249
        • Massafra M
        • Passalacqua MI
        • Gebbia V
        • et al.
        Immunotherapeutic Advances for NSCLC.
        Biologics. 2021; 15: 399-417
        • Bray F
        • Ferlay J
        • Soerjomataram I
        • et al.
        Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
        CA Cancer J Clin. 2018; 68: 394-424
        • Morgensztern D
        • Ng SH
        • Gao F
        • et al.
        Trends in stage distribution for patients with non-small cell lung cancer a national cancer database survey.
        J Thorac Oncol. 2010; 5: 29-33
        • Stenbygaard LE
        • Sorensen JB
        • Larsen H
        • et al.
        Metastatic pattern in non-resectable non-small cell lung cancer.
        Acta Oncologica (Stockholm). 1999; 38: 993-998
        • Tas F
        • Aydiner A
        • Topuz E
        • et al.
        Factors influencing the distribution of metastases and survival in extensive disease small cell lung cancer.
        Acta Oncologica (Stockholm). 1999; 38: 1011-1015
        • Choi MG
        • Choi CM
        • Lee DH
        • et al.
        Different prognostic implications of hepatic metastasis according to front-line treatment in non-small cell lung cancer: a real-world retrospective study.
        Transl Lung Cancer Res. 2021; 10 (-+): 2551
        • Balachandran VP
        • Gonen M
        • Smith JJ
        • et al.
        Nomograms in oncology: more than meets the eye.
        Lancet Oncol. 2015; 16 (E173-E80)
        • Riihimaeki M
        • Hemminki A
        • Fallah M
        • et al.
        Metastatic sites and survival in lung cancer.
        Lung Cancer. 2014; 86: 78-84
        • Rebuzzi SE
        • Alfieri R
        • La Monica S
        • et al.
        Combination of EGFR-TKIs and chemotherapy in advanced EGFR mutated NSCLC: review of the literature and future perspectives.
        Crit Rev Oncol Hematol. 2020; 146102820
        • Guo Y
        • Song J
        • Wang Y
        • et al.
        Concurrent genetic alterations and other biomarkers predict treatment efficacy of EGFR-TKIs in EGFR-mutant non-small cell lung cancer: a review.
        Front Oncol. 2020; 10610923
        • Recondo G
        • Facchinetti F
        • Olaussen KA
        • et al.
        Making the first move in EGFR-driven or ALK-driven NSCLC: first-generation or next-generation TKI?.
        Nat Rev Clin Oncol. 2018; 15: 694-708
        • Hu F
        • Li CH
        • Xu JL
        • et al.
        Additional local consolidative therapy has survival benefit over EGFR tyrosine kinase inhibitors alone in bone oligometastatic lung adenocarcinoma patients.
        Lung Cancer. 2019; 135: 138-144
        • Zhao N
        • X-c Zhang
        • H-h Yan
        • et al.
        Efficacy of epidermal growth factor receptor inhibitors versus chemotherapy as second-line treatment in advanced non-small-cell lung cancer with wild-type EGFR: a meta-analysis of randomized controlled clinical trials.
        Lung Cancer. 2014; 85: 66-73
        • Mak RH
        • Doran E
        • Muzikansky A
        • et al.
        Outcomes after combined modality therapy for EGFR-mutant and wild-type locally advanced NSCLC.
        Oncologist. 2011; 16: 886-895
        • Sequist LV
        • Han JY
        • Ahn MJ
        • et al.
        Osimertinib plus savolitinib in patients with EGFR mutation-positive, MET-amplified, non-small-cell lung cancer after progression on EGFR tyrosine kinase inhibitors: interim results from a multicentre, open-label, phase 1b study.
        Lancet Oncol. 2020; 21: 373-386
        • Girard N
        • Sima CS
        • Jackman DM
        • et al.
        Nomogram to predict the presence of EGFR activating mutation in lung adenocarcinoma.
        Eur Resp J. 2012; 39: 366-372
        • Song J
        • Shi J
        • Dong D
        • et al.
        A new approach to predict progression-free survival in stage IV EGFR-mutant NSCLC patients with EGFR-TKI therapy.
        Clinical Cancer Res. 2018; 24: 3583-3592
        • Velazquez ER
        • Parmar C
        • Liu Y
        • et al.
        Somatic mutations drive distinct imaging phenotypes in lung cancer.
        Cancer Res. 2017; 77: 3922-3930
        • Park H
        • Kim KA
        • Jung JH
        • et al.
        MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer.
        Eur Radiol. 2020; 30: 4201-4211
        • Liu ZY
        • Wang S
        • Dong D
        • et al.
        The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges.
        Theranostics. 2019; 9: 1303-1322
        • Liu Y
        • Kim J
        • Balagurunathan Y
        • et al.
        Radiomic features are associated with EGFR mutation status in lung adenocarcinomas.
        Clin Lung Cancer. 2016; 17: 441-448
        • Gevaert O
        • Echegaray S
        • Khuong A
        • et al.
        Predictive radiogenomics modeling of EGFR mutation status in lung cancer.
        Sci Rep-Uk. 2017; 7
        • Yuan M
        • Pu XH
        • Xu XQ
        • et al.
        Lung adenocarcinoma: Assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image.
        J Magn Reson Imaging. 2017; 46: 281-289
        • Zhang LW
        • Chen BJ
        • Liu X
        • et al.
        Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer.
        Transl Oncol. 2018; 11: 94-101
        • Pinheiro G
        • Pereira T
        • Dias C
        • et al.
        Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS.
        Sci Rep-Uk. 2020; 10: 3625
        • Wang GY
        • Wang BM
        • Wang Z
        • et al.
        Radiomics signature of brain metastasis: prediction of EGFR mutation status.
        Eur Radiol. 2021; 31: 4538-4547
        • Chen BHT
        • Jin TH
        • Ye NR
        • et al.
        Predicting survival duration with MRI radiomics of brain metastases from non-small cell lung cancer.
        Front Oncol. 2021; 11: 621088
        • Jiang XR
        • Ren MH
        • Shuang X
        • et al.
        Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma.
        J Magn Reson Imaging. 2021; 54: 497-507
        • Fan Y
        • Dong Y
        • Yang HZ
        • et al.
        Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer.
        Physics in Medicine and Biology. 2021; 66: 215008
        • Cao R
        • Dong Y
        • Wang X
        • et al.
        MRI-based radiomics nomogram as a potential biomarker to predict the EGFR mutations in exon 19 and 21 based on thoracic spinal metastases in lung adenocarcinoma.
        Acad Radiol. 2021; 29 (3): E9-E17
        • Wu S
        • Shen G
        • Mao J
        • et al.
        CT radiomics in predicting EGFR mutation in non-small cell lung cancer: a single institutional study.
        Front Oncol. 2020; 10542957
        • Li X
        • Yin G
        • Zhang Y
        • et al.
        Predictive power of a radiomic signature based on (18)F-FDG PET/CT images for EGFR mutational status in NSCLC.
        Front Oncol. 2019; 9: 1062
        • van Griethuysen JJM
        • Fedorov A
        • Parmar C
        • et al.
        Computational radiomics system to decode the radiographic phenotype.
        Cancer Res. 2017; 77: E104-E1E7
        • Sauerbrei W
        • Royston P
        • Binder H.
        Selection of important variables and determination of functional form for continuous predictors in multivariable model building.
        Stat Med. 2007; 26: 5512-5528
        • DeLong ER
        • DeLong DM
        • Clarke-Pearson DL.
        Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
        Biometrics. 1988; 44: 837-845
        • Fiz F
        • Vigano L
        • Gennaro N
        • et al.
        Radiomics of liver metastases: a systematic review.
        Cancers. 2020; 12: 2881
        • Wei J
        • Jiang H
        • Gu D
        • et al.
        Radiomics in liver diseases: current progress and future opportunities.
        Liver Int. 2020; 40: 2050-2063
        • Granata V
        • Fusco R
        • Barretta ML
        • et al.
        Radiomics in hepatic metastasis by colorectal cancer.
        Infect Agents Cancer. 2021; 16
        • Tu W
        • Sun G
        • Fan L
        • et al.
        Radiomics signature: a potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology.
        Lung Cancer. 2019; 132: 28-35
        • Mei D
        • Luo Y
        • Wang Y
        • et al.
        CT texture analysis of lung adenocarcinoma: can radiomic features be surrogate biomarkers for EGFR mutation statuses.
        Cancer Imaging. 2018; 18: 52
        • Digumarthy SR
        • Padole AM
        • Lo Gullo R
        • et al.
        Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?.
        Medicine. 2019; 98: E13963
        • Castellano G
        • Bonilha L
        • Li LM
        • et al.
        Texture analysis of medical images.
        Clin Radiol. 2004; 59: 1061-1069
        • Mu W
        • Jiang L
        • Zhang J
        • et al.
        Non-invasive decision support for NSCLC treatment using PET/CT radiomics.
        Nat Commun. 2020; 11: 5228
        • Wang S
        • Shi J
        • Ye Z
        • et al.
        Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.
        Eur Resp J. 2019; 53: 1800986
        • Dou TH
        • Coroller TP
        • van Griethuysen JJM
        • et al.
        Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC.
        PLoS One. 2018; 13: E0206108
        • Das SK
        • Fang K-W
        • Xu L
        • et al.
        Integrative nomogram of intratumoral, peritumoral, and lymph node radiomic features for prediction of lymph node metastasis in cT1N0M0 lung adenocarcinomas.
        Sci Rep-Uk. 2021; 11: 10829
        • Xiang ZL
        • Zeng ZC
        • Fan J
        • et al.
        Gene expression profiling of fixed tissues identified hypoxia-inducible factor-1alpha, VEGF, and matrix metalloproteinase-2 as biomarkers of lymph node metastasis in hepatocellular carcinoma.
        Clin Cancer Res. 2011; 17: 5463-5472
        • Wang H
        • Chen L.
        Tumor microenviroment and hepatocellular carcinoma metastasis.
        J Gastroenterol Hepatol. 2013; 28: 43-48
        • van Zijl F
        • Mair M
        • Csiszar A
        • et al.
        Hepatic tumor-stroma crosstalk guides epithelial to mesenchymal transition at the tumor edge.
        Oncogene. 2009; 28: 4022-4033
        • Shi Z
        • Zheng X
        • Shi R
        • et al.
        Radiological and clinical features associated with epidermal growth factor receptor mutation status of exon 19 and 21 in lung adenocarcinoma.
        Sci Rep. 2017; 7: 364
        • Shen TX
        • Liu L
        • Li WH
        • et al.
        CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma.
        Cancer Imaging. 2019; 19: 34
        • Liu G
        • Xu Z
        • Ge Y
        • et al.
        3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma.
        Transl Lung Cancer Res. 2020; 9: 1212-1224