Biomechanical parameters quantified by MR elastography for predicting response to neoadjuvant chemotherapy and...

Little is known regarding biomechanical properties derived from multifrequency MR elastography temporal changes during neoadjuvant chemotherapy (NAC) and associated with pathologic complete response (pCR) and disease-free survival (DFS) in breast cancer. We aimed to investigate temporal changes in NAC-associated biomechanical parameters and assess biomechanical parameters as a predictor of pCR and DFS in breast cancer.

In this prospective longitudinal study, participants with breast cancer who received NAC were enrolled from February 2021 to May 2023. All participants underwent multifrequency MR-elastography at four timepoints: before NAC (T1) and after 2 (T2), 4 (T3), and 6 (T4) cycles. Tomoelastography postprocessing provided biomechanical maps of shear-wave-speed (c) and loss-angle (φ) as proxies of stiffness and viscosity. The biomechanical parameters were validated by means of correlation with histopathologic measurements. Generalized estimating equations were used to compare temporal changes in biomechanical parameters at four time points. Logistic regression was used for pCR analysis and Cox proportional hazards regression was used for survival analysis. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) analysis.

A total of 235 women (50.6 ± 7.9 years) with 964 scans were enrolled. Biomechanical parameters were supported by positive correlations with pathologic examination-based stroma fraction (c: r =.76, P <.001; φ: r =.49, P =.008) and cellularity (c: r =.58, P =.001; φ: r =.40, P =.035). Progesterone receptor, human epidermal growth factor receptor-2 (HER2), T2-c, and T2-φ were independently associated with pCR (all P <.05). Estrogen receptor, HER2, clinical stage, and change in φ at the early stage of NAC were associated with PFS (all P <.05). The predictive model, which incorporated biomechanical parameters and clinicopathologic characteristics significantly outperformed the clinicopathologic model in predicting pCR (AUC: 0.95, 95% confidence interval [CI]: 0.92, 0.98 vs. 0.79, 95%CI: 0.73, 0.84; P <.001). The predictive model also showed good discrimination ability for DFS (C-index = 0.82, 95%CI: 0.72, 0.90) and stratified prognosis into low-risk and high-risk groups (log-rank, P <.001).

During NAC, patients with higher tumor stiffness and viscosity are less likely to achieve DFS and pCR. The biomechanical parameters exhibit excellent biological interpretability and serve as valuable biomarkers for predicting pCR and DFS in patients with breast cancer.