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European Radiology

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01-11-2018 | Magnetic Resonance

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

Authors: Rafael Ortiz-Ramón, Andrés Larroza, Silvia Ruiz-España, Estanislao Arana, David Moratal

Published in: European Radiology | Issue 11/2018

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.

Available only for authorised users

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Title: Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study
Authors: Rafael Ortiz-Ramón
Andrés Larroza
Silvia Ruiz-España
Estanislao Arana
David Moratal
Publication date: 01-11-2018
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 11/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5463-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objective

Methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusion

Key Points

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