Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence

Jieun Koh, Eunjung Lee, Kyunghwa Han, Sujeong Kim, Dong kyu Kim, Jin Young Kwak, Jung Hyun Yoon, Hee Jung Moon

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14 Citations (Scopus)


This paper evaluated 3-dimensional radiomics features of breast magnetic resonance imaging (MRI) as prognostic factors for predicting systemic recurrence in triple-negative breast cancer (TNBC) and validated the results with a different MRI scanner. The Rad score was generated from 3-dimensional radiomic features of MRI for 231 TNBCs (training set (GE scanner), n = 182; validation set (Philips scanner), n = 49). The Clinical and Rad models to predict systemic recurrence were built up and the models were externally validated. In the training set, the Rad score was significantly higher in the group with systemic recurrence (median, −8.430) than the group without (median, −9.873, P < 0.001). The C-index of the Rad model to predict systemic recurrence in the training set was 0.97, which was significantly higher than in the Clinical model (0.879; P = 0.009). When the models were externally validated, the C-index of the Rad model was 0.848, lower than the 0.939 of the Clinical model, although the difference was not statistically significant (P = 0.100). The Rad model for predicting systemic recurrence in TNBC showed a significantly higher C-index than the Clinical model. However, external validation with a different MRI scanner did not show the Rad model to be superior over the Clinical model.

Original languageEnglish
Article number2976
JournalScientific reports
Issue number1
Publication statusPublished - 2020 Dec 1

Bibliographical note

Funding Information:
This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) by the Ministry of Education (2018R1D1A1B07049378). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2020, The Author(s).

All Science Journal Classification (ASJC) codes

  • General


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