Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models

Background To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters,...

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Veröffentlicht in:Journal of translational medicine 2021-06, Vol.19 (1), p.1-236, Article 236
Hauptverfasser: Suo, Shiteng, Yin, Yan, Geng, Xiaochuan, Zhang, Dandan, Hua, Jia, Cheng, Fang, Chen, Jie, Zhuang, Zhiguo, Cao, Mengqiu, Xu, Jianrong
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Sprache:eng
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Zusammenfassung:Background To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables. Methods In this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed. Results Of the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change ([DELA]ADC.sub.200,1000) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment [DELA]ADC.sub.200,1000 improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001). Conclusion Mono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer. Keywords: Breast cancer, Diffusion-weighted MRI, Pathologic complete response, Neoadjuvant chemotherapy, Predictive model
ISSN:1479-5876
1479-5876
DOI:10.1186/s12967-021-02886-3