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European Radiology

, Volume 28, Issue 11, pp 4514–4523 | Cite as

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

  • Rafael Ortiz-Ramón
  • Andrés Larroza
  • Silvia Ruiz-España
  • Estanislao Arana
  • David Moratal
Magnetic Resonance

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.

Keywords

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

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

Notes

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).

Compliance with ethical standards

Guarantor

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

Supplementary material

330_2018_5463_MOESM1_ESM.docx (35 kb)
ESM 1 (DOCX 34 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Centre for Biomaterials and Tissue EngineeringUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Department of MedicineUniversitat de ValènciaValenciaSpain
  3. 3.Department of RadiologyFundación Instituto Valenciano de OncologíaValenciaSpain

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