Abstract:
RATIONALE AND OBJECTIVES:Ki-67 is one of the most important biomarkers of breast cancer traditionally measured invasively via immunohistochemistry. In this study, deep learning based radiomics models were established for preoperative prediction of Ki-67 status using multiparametric magnetic resonance imaging (mp-MRI). MATERIALS AND METHODS:Total of 328 eligible patients were retrospectively reviewed [training dataset (n = 230) and a temporal validation dataset (n = 98)]. Deep learning imaging features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (T1+C). Transfer learning techniques constructed four feature sets based on the individual three MR sequences and their combination (i.e., mp-MRI). Multilayer perceptron classifiers were trained for final prediction of Ki-67 status. Mann-Whitney U test compared the predictive performance of individual models. RESULTS:The area under curve (AUC) of models based on T2WI,T1+C,DWI and mp-MRI were 0.727, 0.873, 0.674, and 0.888 in the training dataset, respectively, and 0.706, 0.829, 0.643, and 0.875 in the validation dataset, respectively. The predictive performance of mp-MRI classification model in the AUC value was significantly better than that of the individual sequence model (all p< 0.01). CONCLUSION:In clinical practice, a noninvasive approach to improve the performance of radiomics in preoperative prediction of Ki-67 status can be provided by extracting breast cancer specific structural and functional features from mp-MRI images obtained from conventional scanning sequences using the advanced deep learning methods. This could further personalize medicine and computer aided diagnosis.
journal_name
Acad Radioljournal_title
Academic radiologyauthors
Liu W,Cheng Y,Liu Z,Liu C,Cattell R,Xie X,Wang Y,Yang X,Ye W,Liang C,Li J,Gao Y,Huang C,Liang Cdoi
10.1016/j.acra.2020.02.006subject
Has Abstractpub_date
2021-02-01 00:00:00pages
e44-e53issue
2eissn
1076-6332issn
1878-4046pii
S1076-6332(20)30092-1journal_volume
28pub_type
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