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

• Published in: European radiology Vol. 32; no. 1; pp. 650 - 660
• Main Authors: 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: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2022; Springer Nature B.V
• Subjects: 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; Medical prognosis; Medicine; Medicine & Public Health; Neural networks; Neuroradiology; Parameters; Performance prediction; Perfusion; Predictions; Radiology; Radiomics; Receptors; Regression analysis; Texture; Tumors; Ultrasound; Breast neoplasms; Biomarkers, tumor; Perfusion imaging; Magnetic resonance imaging; Machine learning

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...