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

Authors
 Ji Young Lee  ;  Kwang-Sig Lee  ;  Bo Kyoung Seo  ;  Kyu Ran Cho  ;  Ok Hee Woo  ;  Sung Eun Song  ;  Eun-Kyung Kim  ;  Hye Yoon Lee  ;  Jung Sun Kim  ;  Jaehyung Cha 
Citation
 EUROPEAN RADIOLOGY, Vol.32(1) : 650-660, 2022-01 
Journal Title
EUROPEAN RADIOLOGY
ISSN
 0938-7994 
Issue Date
2022-01
MeSH
Bayes Theorem ; Breast Neoplasms* / diagnostic imaging ; Female ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Prognosis ; Prospective Studies ; Retrospective Studies
Keywords
Biomarkers, tumor ; Breast neoplasms ; Machine learning ; Magnetic resonance imaging ; Perfusion imaging
Abstract
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 (Ve).

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 (Ve) among perfusion parameters.
Full Text
https://link.springer.com/article/10.1007/s00330-021-08146-8
DOI
10.1007/s00330-021-08146-8
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187857
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