Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

Abstract

Objective

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach.

Methods

Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one.

Results

In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180).

Conclusion

Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels.

Key Points

• Texture analysis is a promising source of biomarkers for classifying brain neoplasms.

• MRI texture features of brain metastases could help identifying the primary cancer.

• Volumetric texture features are more discriminative than traditional 2D texture features.

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Abbreviations

ANOVA:

Analysis of variance

AUC:

Area under receiver operating characteristic curve

BM:

Brain metastases

CM:

Confusion matrix

CV:

Cross-validation

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

LGOCV:

Leave-group-out cross-validation

NGL:

Number of gray-levels

NGTDM:

Neighborhood gray-tone difference matrix

RF:

Random Forest

TA:

Texture analysis

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Funding

This work has been partially funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R.

Rafael Ortiz-Ramón was supported by grant ACIF/2015/078 from the Conselleria d’Educació, Investigació, Cultura i Esport of the Valencian Community (Spain).

Andrés Larroza was supported by grant FPU12/01140 from the Spanish Ministerio de Educación, Cultura y Deporte (MECD).

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Correspondence to David Moratal.

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The scientific guarantor of this publication is David Moratal.

Conflict of interest

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.

Statistics and biometry

One of the authors (David Moratal) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Ortiz-Ramón, R., Larroza, A., Ruiz-España, S. et al. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 28, 4514–4523 (2018). https://doi.org/10.1007/s00330-018-5463-6

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Keywords

  • Neoplasms, Unknown primary
  • Magnetic resonance imaging
  • Image processing, Computer-assisted
  • Biomarkers
  • Feasibility studies