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Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface

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A Correction to this article was published on 03 March 2023

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Abstract

Purpose

To evaluate the potential of subregional radiomics as a novel tumor marker in predicting epidermal growth factor receptor (EGFR) mutation status and response to EGFR-tyrosine kinase inhibitor (TKI) therapy in NSCLC patients with brain metastasis (BM).

Materials and methods

We included 230 patients from center 1, and 80 patients were included from center 2 to form a primary and external validation cohort, respectively. Patients underwent contrast-enhanced T1-weighted and T2-weighted MRI scans before treatment. The individual- and population-level clustering was used to partition the peritumoral edema area (POA) into phenotypically consistent subregions. Radiomics features were calculated and selected from the tumor active area (TAA), POA and subregions, and used to develop models. Prediction values of each region were investigated and compared with receiver operating characteristic curves and Delong test.

Results

For predicting EGFR mutations, a multi-region combined model (EGFR-Fusion) was developed based on joint of the partitioned metastasis/brain parenchyma (M/BP)-interface and TAA, and generated the highest prediction performance in the training (AUC = 0.945, SEN = 0.878, SPE = 0.937), internal validation (AUC = 0.880, SEN = 0.733, SPE = 0.969), and external validation (AUC = 0.895, SEN = 0.875, SPE = 0.800) cohorts. For predicting response to EGFR-TKI, the developed multi-region combined model (TKI-Fusion) yielded predictive AUCs of 0.869 (SEN = 0.717, SPE = 0.884), 0.786 (SEN = 0.708, SPE = 0.818), and 0.802 (SEN = 0.750, SPE = 0.800) in the training, internal validation and external validation cohort, respectively.

Conclusion

Our study revealed that complementary information regarding the EGFR status and response to EGFR-TKI can be provided by subregional radiomics. The proposed radiomics models may be new markers to guide treatment plans for NSCLC patients with BM.

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Abbreviations

AIC:

Akaike information criterion

AUC:

Area under the curve

BM:

Brain metastasis

EGFR:

Epidermal growth factor receptor

ICC:

Intraclass correlation coefficients

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

M/BP:

Metastasis/brain parenchyma

NSCLC:

Non-small-cell lung carcinoma

ROI:

Region of interest

T1C:

Contrast-enhanced T1-weighted imaging

T2W:

T2-weighted imaging

TAA:

Tumor active area

POA:

Peritumoral edema area

SC:

Silhouette coefficient

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Acknowledgements

The research was funded by Natural Science Foundation of Liaoning Province (2021-MS-205), Climbing Fund of National Cancer Center (NCC201806B011), 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, Zilong Zhao and Xingling Wang have contributed equally to this work.

Authors and Affiliations

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

    Ying Fan, Hua Ai, Chunna Yang & Xiran Jiang

  2. Department of Neurosurgery, The First Affiliated Hospital of China Medical University, Shenyang, 110001, People’s Republic of China

    Zilong Zhao

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

    Xingling Wang

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

    Yahong Luo

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Contributions

YF and XJ contributed to the conception and design of the study. ZZ, XW and YL contributed to the acquisition of clinical data. YF, ZZ and XJ contributed to data analysis and interpretation. HA, XW and CY contributed to statistical analyses. XJ and YF participated in manuscript preparation, edition and revision. All authors participated in the review of manuscript. All authors have read and approved the final manuscript.

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Correspondence to Yahong Luo or Xiran Jiang.

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This retrospective study was approved by the Institutional Review Board of the Cancer Hospital of China Medical University, and the consents from patients were waived.

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Fan, Y., Zhao, Z., Wang, X. et al. Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface. Radiol med 127, 1342–1354 (2022). https://doi.org/10.1007/s11547-022-01569-3

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