Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI

Objectives To investigate machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and angiogenesis properties on magnetic resonance imaging (MRI). Methods This prospective study examined 2...

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Veröffentlicht in:	European radiology 2022-01, Vol.32 (1), p.650-660
Hauptverfasser:	Lee, Ji Young, Lee, Kwang-sig, Seo, Bo Kyoung, Cho, Kyu Ran, Woo, Ok Hee, Song, Sung Eun, Kim, Eun-Kyung, Lee, Hye Yoon, Kim, Jung Sun, Cha, Jaehyung
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Angiogenesis Artificial neural networks Bayesian analysis Biomarkers Breast Breast cancer Decision trees Diagnostic Radiology Entropy Epidermal growth factor Growth factors Heterogeneity Imaging Internal Medicine Interventional Radiology Invasiveness Irregularities Learning algorithms Machine learning Magnetic properties Magnetic resonance imaging Mathematical models Medicine Medicine & Public Health Neural networks Neuroradiology Parameters Performance prediction Perfusion Predictions Radiology Radiomics Receptors Regression analysis Texture Tumors Ultrasound
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