0000000000676213

AUTHOR

Rafael Ortiz-ramon

showing 2 related works from this author

Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach

2017

Detection of brain metastases in patients with undiagnosed primary cancer is unusual but still an existing phenomenon. In these cases, identifying the cancer site of origin is non-feasible by visual examination of magnetic resonance (MR) images. Recently, radiomics has been proposed to analyze differences among classes of visually imperceptible imaging characteristics. In this study we analyzed 46 T1-weighted MR images of brain metastases from 29 patients: 29 of lung and 17 of breast origin. A total of 43 radiomics texture features were extracted from the metastatic lesions. Support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers were implemented to evaluate the classificati…

Pathologymedicine.medical_specialtyLungmedicine.diagnostic_testbusiness.industryFeature extractionCancerMagnetic resonance imagingmedicine.disease030218 nuclear medicine & medical imagingSupport vector machine03 medical and health sciences0302 clinical medicineBreast cancermedicine.anatomical_structureRadiomicsmedicineRadiologybusinessQuantization (image processing)030217 neurology & neurosurgery2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
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A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma

2017

[EN] Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictiv…

medicine.medical_specialtyMetastatic lesionsLung Neoplasms030218 nuclear medicine & medical imagingTECNOLOGIA ELECTRONICA03 medical and health sciencesNaive Bayes classifier0302 clinical medicineRadiomicsmedicineHumansLung cancerMelanomaSite of originmedicine.diagnostic_testbusiness.industryBrain NeoplasmsMelanomaMagnetic resonance imagingBayes Theoremmedicine.diseasePrimary cancerMagnetic Resonance Imaging030220 oncology & carcinogenesisRadiologybusiness
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