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
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-021-08146-8

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 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived. Results Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes ( p < 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes ( p < 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space ( V e ). Conclusions Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer. Key Points • Machine learning, integrating tumor heterogeneity and angiogenesis properties on MRI, can be applied to predict prognostic biomarkers and molecular subtypes in breast cancer. • The random forest model showed the best predictive performance among the five machine learning models (logistic regression, decision tree, naïve Bayes, random forest, and artificial neural network). • The most important MRI parameters for predicting prognostic factors in breast cancer were texture irregularity (entropy) among texture parameters and relative extracellular extravascular space (V e ) among perfusion parameters.