عنوان انگلیسی مقاله:
Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting
ترجمه فارسی عنوان مقاله:
پیش بینی نوع های مولکولی سرطان پستان با استفاده از ویژگی های BI-RADS مبتنی بر رویکرد یادگیری ماشین "جعبه سفید" در یک تصویربرداری چند حالته
Sciencedirect - Elsevier - European Journal of Radiology, 114 (2019) 175-184: doi:10:1016/j:ejrad:2019:03:015
Mingxiang Wua, Xiaoling Zhonga, Quanzhou Pengb, Mei Xua, Shelei Huanga, Jialin Yuana, Jie Maa,⁎, Tao Tanc,
Purpose: To develop and validate an interpretable and repeatable machine learning model approach to predict
molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI
Methods: We retrospectively assessed 363 breast cancer cases (Luminal A 151, Luminal B 96, HER2 76, and BLBC
40). Eighty-two features defined in the BI-RADS lexicon were visually described. A decision tree model with the
Chi-squared automatic interaction detector (CHAID) algorithm was applied for feature selection and classification.
A 10-fold cross-validation was performed to investigate the performance (i.e., accuracy, positive predictive
value, sensitivity, and F1-score) of the decision tree model.
Results: Seven of the 82 variables were derived from the decision tree-based feature selection and used as features
for the classification of molecular subtypes including mass margin calcification on mammography, mass
margin types of kinetic curves in the delayed phase, mass internal enhancement characteristics, non-mass enhancement
distribution on MRI, and breastfeeding history. The decision tree model accuracy was 74.1%. For
each molecular subtype group, Luminal A achieved a sensitivity, positive predictive value, and F1-score of
79.47%, 75.47%, and 77.42%, respectively; Luminal B showed a sensitivity, positive predictive value, and F1-
score of 64.58%, 55.86%, and 59.90%, respectively; HER2 had a sensitivity, positive predictive value, and F1-
scores of 81.58%, 95.38%, and 87.94%, respectively; BLBC showed sensitivity, positive predictive value, and F1-
scores of 62.50%, 89.29%, and 73.53%, respectively.
Conclusions: We applied a complete “white box” machine learning method to predict the molecular subtype of
breast cancer based on the BI-RADS feature description in a multi-modal setting. By combining BI-RADS features
in both mammography and MRI, the prediction accuracy is boosted and robust. The proposed method can be
easily applied widely regardless of variability of imaging vendors and settings because of the applicability and
acceptance of the BI-RADS.
Keywords: Breast cancer | Molecular subtype | MRI | Mammography | Decision tree | Machine learning