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Fusion of 3D Radiomic Features from Multiparametric Magnetic Resonance Images for Breast Cancer Risk Classification

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Smart Technologies, Systems and Applications (SmartTech-IC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1154))

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Abstract

Radiomics imaging technology refers to the computation of a large number of quantitative features to describe characteristics of medical images that specialists can not appreciate. The quality of Magnetic Resonance Images (MRI) allows capturing pathology features through different sequences acquired from the target tissue. The use of multiparametric MRI has shown to be useful in the diagnosis of breast cancer but is challenging to radiomic analysis by the fusion of information from different images. In this work, 3D radiomic features extracted from nine breast MRI sequences were used to train a model able to discriminate positive and negative breast masses in the region of interest manually identified. Two fusion strategies were here evaluated; in the first one, features from all sequences were concatenated, and Fisher-Score and Gini-Index feature selectors were used to identify the most discriminative features; in the second one, features were initially selected from each sequence and then were concatenated to be used for training a classification model; Random Forest and a Support Vector Machine learning models were also evaluated. To test both fusion strategies was used a database with 146 Interest volumes (VoIs) from which, 61 were positive and 85 negative findings. Although there are differences in the accuracy obtained by the two fusion strategies, these are not significant and it was clear that a small number of features provide better performance than using the whole set of them. The best performance was reached using Random Forest with the 20% of the computed features and selected by the Gini/index algorithm, With which an AUC of 73.6% was obtained.

Supported by Colciencias, Instituto Tecnológico Metropolitano, and Instituto de Alta Tecnología Médica. Project RC740-2017.

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Author information

Authors and Affiliations

  1. Instituto Tecnológico Metropolitano, Medellín, Colombia

    Diana M. Marín-Castrillón, Jaider Stiven Rincón, Andrés E. Castro-Ospina & Gloria M. Díaz

  2. Instituto de Alta Tecnología Médica, Medellín, Colombia

    Liliana Hernández

Authors
  1. Diana M. Marín-Castrillón
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  2. Jaider Stiven Rincón
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  3. Andrés E. Castro-Ospina
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  4. Liliana Hernández
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  5. Gloria M. Díaz
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Corresponding author

Correspondence to Gloria M. Díaz .

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Editors and Affiliations

  1. Universidad Politécnica Salesiana, Quito, Ecuador

    Fabián R. Narváez

  2. Universidad Politécnica Salesiana, Quito, Ecuador

    Diego F. Vallejo

  3. Universidad Politécnica Salesiana, Quito, Ecuador

    Paulina A. Morillo

  4. Universidad Politécnica Salesiana, Quito, Ecuador

    Julio R. Proaño

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Marín-Castrillón, D.M., Rincón, J.S., Castro-Ospina, A.E., Hernández, L., Díaz, G.M. (2020). Fusion of 3D Radiomic Features from Multiparametric Magnetic Resonance Images for Breast Cancer Risk Classification. In: Narváez, F., Vallejo, D., Morillo, P., Proaño, J. (eds) Smart Technologies, Systems and Applications. SmartTech-IC 2019. Communications in Computer and Information Science, vol 1154. Springer, Cham. https://doi.org/10.1007/978-3-030-46785-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-46785-2_21

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  • Online ISBN: 978-3-030-46785-2

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