Translational cancer research · 2026
Development and multicenter validation of an immunoinflammatory marker-MRI model for pathological complete response after neoadjuvant chemotherapy in invasive breast cancer.
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Meng Fan, Deng Yanfang, Yuan Junhui, Fang Shaobo, Zhang Hongkai, Chen Tiandong, Zhang Renzhi, Xue Yunjing, Chen Xuejun, Qu Jinrong
Аннотация
BACKGROUND: Accurate noninvasive prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in invasive breast cancer (BC) remains challenging. This study aimed to develop and validate a multivariable prediction model integrating clinicopathological variables, immunoinflammatory markers, and multiparametric magnetic resonance imaging (MRI) features for predicting pCR after NAC.
METHODS: In this retrospective multicenter study, 345 women with invasive BC who underwent pre-treatment breast MRI and NAC were included. pCR was defined as the absence of residual invasive cancer in the breast and axillary lymph nodes at surgery. Patients were divided into training and internal validation cohorts, with an independent external cohort used for validation. Clinicopathological variables, immunoinflammatory markers, and MRI features, including mean apparent diffusion coefficient (ADCmean), were collected. Predictor selection was performed using the least absolute shrinkage and selection operator and multivariable logistic regression. Model performance was assessed using receiver operating characteristic analysis.
RESULTS: Among the 345 patients, 116 (33.62%) achieved pCR. Clinical T stage, lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), enhancement pattern, and ADCmean were independent predictors. The combined model showed the best performance, with area under the curves of 0.820, 0.810, and 0.799 in the training, internal validation, and external validation cohorts, outperforming the clinical and MRI models.
CONCLUSIONS: The combined model integrating clinicopathological variables, immunoinflammatory markers, and multiparametric MRI features may help predict pCR after NAC and support individualized treatment planning and potential surgical de-escalation.
METHODS: In this retrospective multicenter study, 345 women with invasive BC who underwent pre-treatment breast MRI and NAC were included. pCR was defined as the absence of residual invasive cancer in the breast and axillary lymph nodes at surgery. Patients were divided into training and internal validation cohorts, with an independent external cohort used for validation. Clinicopathological variables, immunoinflammatory markers, and MRI features, including mean apparent diffusion coefficient (ADCmean), were collected. Predictor selection was performed using the least absolute shrinkage and selection operator and multivariable logistic regression. Model performance was assessed using receiver operating characteristic analysis.
RESULTS: Among the 345 patients, 116 (33.62%) achieved pCR. Clinical T stage, lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), enhancement pattern, and ADCmean were independent predictors. The combined model showed the best performance, with area under the curves of 0.820, 0.810, and 0.799 in the training, internal validation, and external validation cohorts, outperforming the clinical and MRI models.
CONCLUSIONS: The combined model integrating clinicopathological variables, immunoinflammatory markers, and multiparametric MRI features may help predict pCR after NAC and support individualized treatment planning and potential surgical de-escalation.