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Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma

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Abstract

Objectives

This study aims to explore values of multi-parametric MRI–based radiomics for detecting the epidermal growth factor receptor (EGFR) mutation and resistance (T790M) mutation in lung adenocarcinoma (LA) patients with spinal metastasis.

Methods

This study enrolled a group of 160 LA patients from our hospital (between Jan. 2017 and Feb. 2021) to build a primary cohort. An external cohort was developed with 32 patients from another hospital (between Jan. 2017 and Jan. 2021). All patients underwent spinal MRI (including T1-weighted (T1W) and T2-weighted fat-suppressed (T2FS)) scans. Radiomics features were extracted from the metastasis for each patient and selected to develop radiomics signatures (RSs) for detecting the EGFR and T790M mutations. The clinical-radiomics nomogram models were constructed with RSs and important clinical parameters. The receiver operating characteristics (ROC) curve was used to evaluate the predication capabilities of each model. Calibration and decision curve analyses (DCA) were constructed to verify the performance of the models.

Results

For detecting the EGFR and T790M mutation, the developed RSs comprised 9 and 4 most important features, respectively. The constructed nomogram models incorporating RSs and smoking status showed favorite prediction efficacy, with AUCs of 0.849 (Sen = 0.685, Spe = 0.885), 0.828 (Sen = 0.964, Spe = 0.692), and 0.778 (Sen = 0.611, Spe = 0.929) in the training, internal validation, and external validation sets for detecting the EGFR mutation, respectively, and with AUCs of 0.0.842 (Sen = 0.750, Spe = 0.867), 0.823 (Sen = 0.667, Spe = 0.938), and 0.800 (Sen = 0.875, Spe = 0.800) in the training, internal validation, and external validation sets for detecting the T790M mutation, respectively.

Conclusions

Radiomics features from the spinal metastasis were predictive on both EGFR and T790M mutations. The constructed nomogram models can be potentially considered as new markers to guild treatment management in LA patients with spinal metastasis.

Key Points

• To our knowledge, this study was the first approach to detect the EGFR T790M mutation based on spinal metastasis in patients with lung adenocarcinoma.

• We identified 13 MRI features that were strongly associated with the EGFR T790M mutation.

• The proposed nomogram models can be considered as potential new markers for detecting EGFR and T790M mutations based on spinal metastasis.

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Abbreviations

AIC:

Akaike information criterion

AUC:

Area under the ROC curve

BM:

Bone metastasis

CEA:

Carcinoembryonic antigen

CYFRA:

Cytokeratin

DCA:

Decision curve analysis

EGFR:

Epidermal growth factor receptor

EGFR-TKIs:

Epidermal growth factor receptor-tyrosine kinase inhibitors

GLCM:

Gray-level co-occurrence matrix

ICC:

Intraclass correlation coefficient

LA:

Lung adenocarcinoma

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

NSE:

Neuron-specific enolase

PS:

Performance status

ROC:

Receiver operating characteristic

ROI:

Region of interest

RS:

Radiomics signature

T1W:

T1-weighted

T2FS:

Fat-suppressed T2-weighted

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Funding

The study was funded by the Climbing Fund of National Cancer Center (NCC201806B011), National Natural Science Foundation of China (81872363), PhD Start-up Fund of Liaoning Province (2021-BS-044), China National Natural Science Foundation (31770147), Natural Science Foundation of Liaoning Province (2021-MS-205), and Medical-Engineering Joint Fund for Cancer Hospital of China Medical University and Dalian University of Technology (LD202029).

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Author notes

  1. Ying Fan and Yue Dong contributed equally to this work.

Authors and Affiliations

  1. School of Intelligent Medicine, China Medical University, Liaoning, 110122, People’s Republic of China

    Ying Fan & Xiran Jiang

  2. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People’s Republic of China

    Yue Dong, Xinyan Sun, Xiaoyu Wang, Peng Zhao & Yahong Luo

  3. Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People’s Republic of China

    Huan Wang

  4. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110004, People’s Republic of China

    Hongbo Wang

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  1. Ying Fan
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  4. Hongbo Wang
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Correspondence to Xiran Jiang.

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The scientific guarantor of this publication is Xiran Jiang, PhD.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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• retrospective

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• multicenter study

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Fan, Y., Dong, Y., Wang, H. et al. Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma. Eur Radiol 32, 6739–6751 (2022). https://doi.org/10.1007/s00330-022-08955-5

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  • DOI: https://doi.org/10.1007/s00330-022-08955-5

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