Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

Kotaro Yoshida, Hiroko Kawashima, Takayuki Kannon, Atsushi Tajima, Naoki Ohno, Kanako Terada, Atsushi Takamatsu, Hayato Adachi, Masako Ohno, Tosiaki Miyati, Satoko Ishikawa, Hiroko Ikeda, Toshifumi Gabata

研究成果: ジャーナルへの寄稿学術論文査読

15 被引用数 (Scopus)

抄録

Purpose: To investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. Methods: Seventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal 1 min – Signal pre)/Signal pre. Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed. Results: The best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features. Conclusions: Pretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.

本文言語英語
ページ(範囲)19-25
ページ数7
ジャーナルMagnetic Resonance Imaging
92
DOI
出版ステータス出版済み - 10-2022
外部発表はい

All Science Journal Classification (ASJC) codes

  • 生物理学
  • 生体医工学
  • 放射線学、核医学およびイメージング

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