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Value of radiomics model based on spatiotemporal heterogeneity of MRI to predict pathological complete response in triple-negative breast cancer
ZHOU Jiayin  YOU Chao  WANG Zezhou  LIN Luyi  SHEN Yiyuan  GU Yajia 

Cite this article as: ZHOU J Y, YOU C, WANG Z Z, et al. Value of radiomics model based on spatiotemporal heterogeneity of MRI to predict pathological complete response in triple-negative breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(1): 28-34. DOI:10.12015/issn.1674-8034.2024.01.005.


[Abstract] Objective To develop a spatiotemporal heterogeneity based radiomics model for the early prediction of pathological complete response (pCR) in triple-negative breast cancer (TNBC).Materials and Methods The data of 173 TNBC patients who received neoadjuvant chemotherapy (NAC) in our hospital from September 2017 to March 2022 were retrospectively analyzed. MRI images of each patient were collected at pretreatment (Pre-) and after two cycle of NAC (During-). The 55 patients from the DUKE university constituted the external validation cohort. Radiomics features were extracted from the intratumoral subregions and peritumoral region to characterize spatial heterogeneity, and the changes of features before and during NAC (Delta-) were calculated to characterize temporal heterogeneity. The radiomics models were developed by least absolute shrinkage and selection operator (LASSO) regression using the Pre-, During-, and Delta- features. Multi-factor logistic regression was used to integrate single-mode models to develop the longitudinal fusion (Stacking) model. The diagnostic performance and clinical application value of models were evaluated by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).Results Finally, 8, 4 and 10 features were respectively selected from the Pre-, During- and Delta- feature sets to construct the models. The Pre- model based on spatial heterogeneity could predict pCR, with area under the curve (AUC) of 0.74, 0.71 and 0.71 in the training set, validation set and external validation set, respectively. In the training and validation sets, the Stacking model achieved the best performance to predict pCR, and the AUC was 0.86 in both sets. DCA indicated that the value of Stacking model was highest in clinical practice.Conclusions Features based on MRI spatial heterogeneity can effectively predict the pCR of TNBC. The longitudinal fusion model integrated spatiotemporal heterogeneity has the potential to further improve the prediction performance.
[Keywords] triple-negative breast cancer；radiomics；spatiotemporal heterogeneity；habitat imaging；neoadjuvant therapy；magnetic resonance imaging

ZHOU Jiayin1, 2   YOU Chao1, 2   WANG Zezhou3   LIN Luyi1, 2   SHEN Yiyuan1, 2   GU Yajia1, 2*  

1 Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China

2 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China

3 Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Shanghai 200032, China

Corresponding author: GU Y J, E-mail: guyajia@126.com

Conflicts of interest   None.

Received  2023-10-07
Accepted  2023-12-25
DOI: 10.12015/issn.1674-8034.2024.01.005
Cite this article as: ZHOU J Y, YOU C, WANG Z Z, et al. Value of radiomics model based on spatiotemporal heterogeneity of MRI to predict pathological complete response in triple-negative breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(1): 28-34. DOI:10.12015/issn.1674-8034.2024.01.005.

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