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CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer

  • Chest Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

To develop a CT texture-based model able to predict epidermal growth factor receptor (EGFR)-mutated, anaplastic lymphoma kinase (ALK)-rearranged lung adenocarcinomas and distinguish them from wild-type tumors on pre-treatment CT scans.

Materials and methods

Texture analysis was performed using proprietary software TexRAD (TexRAD Ltd, Cambridge, UK) on pre-treatment contrast-enhanced CT scans of 84 patients with metastatic primary lung adenocarcinoma. Textural features were quantified using the filtration-histogram approach with different spatial scale filters on a single 5-mm-thick central slice considered representative of the whole tumor. In order to deal with class imbalance regarding mutational status percentages in our population, the dataset was optimized using the synthetic minority over-sampling technique (SMOTE) and correlations with textural features were investigated using a generalized boosted regression model (GBM) with a nested cross-validation approach (performance averaged over 1000 resampling episodes).

Results

ALK rearrangements, EGFR mutations and wild-type tumors were observed in 19, 28 and 37 patients, respectively, in the original dataset. The balanced dataset was composed of 171 observations. Among the 29 original texture variables, 17 were employed for model building. Skewness on unfiltered images and on fine texture was the most important features. EGFR-mutated tumors showed the highest skewness while ALK-rearranged tumors had the lowest values with wild-type tumors showing intermediate values. The average accuracy of the model calculated on the independent nested validation set was 81.76% (95% CI 81.45–82.06).

Conclusion

Texture analysis, in particular skewness values, could be promising for noninvasive characterization of lung adenocarcinoma with respect to EGFR and ALK mutations.

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Abbreviations

ALK:

Anaplastic lymphoma kinase

CT:

Computed tomography

CTTA:

CT texture analysis

EGFR:

Epidermal growth factor receptor

FBP:

Filtered back-projection

FFPE:

Formalin-fixed paraffin-embedded

FISH:

Fluorescence in situ hybridization

FOV:

Field of view

GBM:

Generalized boosted regression model

KV:

Kilovolt

MAS:

Milliampere per seconds

ML:

Milliliters

MRMR:

Minimum redundancy maximal relevancy filter

NSCLC:

Non-small cell lung cancer

S:

Seconds

SD:

Standard deviation

MOTE:

Synthetic minority oversampling technique

SSF:

Spatial scale filter

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Radiology, University of Brescia, Piazzale Spedali Civili 1, 25123, Brescia, Italy

    Giorgio Maria Agazzi, Marco Ravanelli, Carlotta Pessina, Roberto Maroldi & Davide Farina

  2. Department of Oncology, University of Brescia, Piazzale Spedali Civili 1, 25123, Brescia, Italy

    Elisa Roca & Alfredo Berruti

  3. Department of Molecular and Translational Medicine, University of Brescia, Piazzale Spedali Civili 1, 25123, Brescia, Italy

    Daniela Medicina, Piera Balzarini & William Vermi

Authors
  1. Giorgio Maria Agazzi
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  2. Marco Ravanelli
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  3. Elisa Roca
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  4. Daniela Medicina
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  5. Piera Balzarini
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  6. Carlotta Pessina
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  7. William Vermi
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  8. Alfredo Berruti
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  9. Roberto Maroldi
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  10. Davide Farina
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Corresponding author

Correspondence to Carlotta Pessina.

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The authors declare that they have no conflict of interest.

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Because of the retrospective nature of this study, the requirement for informed consent was waived by the local institutional review board of University of Brescia.

Ethical approval

Ethical approval was waived by the local institutional review board of University of Brescia (NP 4424) in view of the retrospective nature of the study and all the procedures being performed were part of the routine care. The study was conducted in accordance with the ethical standards of the Declaration of Helsinki.

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Agazzi, G.M., Ravanelli, M., Roca, E. et al. CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer. Radiol med 126, 786–794 (2021). https://doi.org/10.1007/s11547-020-01323-7

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  • DOI: https://doi.org/10.1007/s11547-020-01323-7

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