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

AI Article Synopsis

  • The study investigates how MRI texture analysis can distinguish the origin of brain metastases from different cancers, including lung, melanoma, and breast cancer.
  • It involves examining 67 untreated brain metastases from 38 cancer patients, utilizing both 2D and 3D texture features to assess their discriminative power.
  • Results show that 3D texture features, especially with 32 gray-levels, improve classification accuracy of brain metastases, particularly between lung and breast cancer, while showing less effectiveness in distinguishing between breast cancer and melanoma.

Article 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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Source
http://dx.doi.org/10.1007/s00330-018-5463-6DOI Listing

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