Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study
To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach.
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.
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).
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.
• 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.
KeywordsNeoplasms, Unknown primary Magnetic resonance imaging Image processing, Computer-assisted Biomarkers Feasibility studies
Analysis of variance
Area under receiver operating characteristic curve
Gray-level co-occurrence matrix
Gray-level run-length matrix
Gray-level size zone matrix
Number of gray-levels
Neighborhood gray-tone difference matrix
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
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.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• performed at one institution
- 9.Zakaria R, Das K, Bhojak M et al (2014) The role of magnetic resonance imaging in the management of brain metastases: diagnosis to prognosis. Cancer Imaging 14:1–8Google Scholar
- 24.Larroza A, Bodí V, Moratal D (2016) Texture analysis in magnetic resonance imaging: review and considerations for future applications. In: Assessment of cellular and organ function and dysfunction using direct and derived MRI methodologies. InTech, Rijeka, Croatia, pp 75–106Google Scholar
- 29.Mayerhoefer ME, Breitenseher MJ, Kramer J et al (2005) Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging 22:674–680CrossRefGoogle Scholar
- 36.Fernández-Delgado M, Cernadas E, Barro S et al (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181Google Scholar
- 37.Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th international conference on Machine learning - ICML ’08. ACM Press, Helsinki, Finland, pp 96–103Google Scholar
- 43.Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach. In: 2017 I.E. 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, pp 1213–1216Google Scholar
- 44.Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Seogwipo, pp 493–496Google Scholar
- 45.Béresová M, Larroza A, Arana E, et al (2017) 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGMA 1–10Google Scholar