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Predicting progression in triple-negative breast cancer patients undergoing neoadjuvant chemotherapy: Insights from
Soo-Yeon Kim1, Jungwoo Woo2, Sewon Lee2
1Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Objective:
To investigate whether radiomic features obtained from the intratumoral and peritumoral regions of pretreatment magnetic resonance imaging (MRI) can predict progression in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC) in comparison with the previously determined clinical score.
Methods:
This single-center retrospective study evaluated 224 women with TNBC who underwent NAC between 2010 and 2019. Women were randomly allocated to the training set (n = 169) for model development and the test set (n = 55) for model validation. The clinical score consisted of the histologic type, Ki-67 index, and degree of edema on T2-weighted imaging. Intratumoral and peritumoral radiomic features were extracted from T2-weighted images and the first- and last-phase images of dynamic contrast-enhanced MRI. The radiomics model was built using only radiomic features, whereas the combined model incorporated the clinical score along with radiomic features. The area under the receiver operating characteristic curve (AUC) was used to assess performance.
Results:
Progression occurred in 18 and five patients in the training and test sets, respectively. The radiomics model selected three radiomic features (two peritumoral and one intratumoral), while the combined model selected the clinical score and five radiomic features (four peritumoral and one intratumoral). Among the total radiomic features, Inverse Difference Normalized of the peritumoral region of the T2-weighted images, reflective of peritumoral heterogeneity, demonstrated the highest level of association with tumor progression. In the test set, the AUC values of the radiomics-only model, the combined model, and the clinical score were 0.592, 0.764, and 0.720, respectively. Compared to the clinical score, the radiomics-only model (0.720 vs. 0.592, p = 0.468) and the combined model (0.720 vs. 0.764, p = 0.553) did not show superior performance.
Conclusion:
The radiomics features were not superior in predicting the progression of TNBC compared to the clinical score, although the peritumoral heterogeneity on T2-weighted images showed a potential.
