乳腺癌新辅助治疗反应性的关联因素及多模态影像学模型预测研究

Associated factors and multimodal radiomics modeling for treatment response to neoadjuvant therapy in breast cancer

  • 摘要:
    背景 新辅助治疗已成为乳腺癌综合治疗的重要组成部分,乳腺超声与增强磁共振(magnetic resonance imaging,MRI)均是预测病理完全缓解(pathological complete response,pCR)的良好检查技术,但单一技术预测效果欠佳。目的 构建多模态影像学预测模型,评估其对乳腺癌新辅助治疗后pCR的预测性能。方法 收集2022年1月1日— 2022年12月31日期间在解放军总医院第一医学中心接受新辅助治疗的乳腺癌患者的临床、超声及病理资料,并在乳腺MRI图像上对病灶进行手动标注。基于术后病理结果,将患者分为完全缓解组(pCR)和非完全缓解组(non-pCR)。基于免疫组化指标、超声及MRI影像特征参数,采用logistic回归方法构建多模态影像学预测模型,利用ROC分析评价训练集和验证集的预测能力,通过校准曲线来验证模型的准确性,应用决策曲线分析(DCA曲线)评估模型的临床实用度。最后,构建列线图以可视化模型结果。结果 共纳入153例患者,其中42例(27.45%)达pCR,通过LASSO回归、单因素、多因素logistic回归筛选出6个与pCR相关的关键特征,分别为人表皮生长因子受体2、细胞核增殖指数、术前时间信号强度曲线、术前表观扩散系数值、核磁基线肿瘤最大体积、超声治疗早期与术前肿瘤最大面积变化率。结合这些特征构建出多模态影像学预测模型,训练集、验证集的AUC值分别为0.866 (95% CI:0.797 ~ 0.935)、0.822(95% CI:0.702 ~ 0.943),提示与单模态技术模型相比,多模态影像学预测模型具有较好的预测效能。结论 经验证,多模态影像学预测模型可准确预测接受新辅助治疗后乳腺癌患者实现pCR的概率,为制定个体化治疗方案提供了循证依据,从而优化治疗决策。

     

    Abstract:
    Background Neoadjuvant therapy has become an integral component of comprehensive breast cancer treatment.
    Both breast ultrasound and contrast-enhanced magnetic resonance imaging (MRI) serve as valuable diagnostic techniques for
    predicting pathological complete response (pCR). However, the predictive performance of either modality alone remains suboptimal.
    The integration of these two imaging modalities with pathological characteristics demonstrates enhanced predictive efficacy for neoadjuvant therapy outcomes, exhibiting a promising synergistic effect. Objective To develop a multimodal imaging-based predictive model for efficient and accurate assessment of pathological complete response (pCR) rates following neoadjuvant therapy in breast cancer.Methods Clinical, ultrasonographic, and pathological data from breast cancer patients undergoing neoadjuvant therapy in the First Medical Center of Chinese PLA General Hospital from January 1 to December 31, 2022 were retrospectively collected, with manual lesion segmentation performed on breast MRI images. Patients were stratified into pathological complete response (pCR) and non-pCR groups based on postoperative histopathological evaluation. Based on immunohistochemical indicators, ultrasound, and MRI imaging feature parameters, a multimodal imaging predictive model was constructed using logistic regression analysis. The predictive performance of the model was evaluated in both training and validation cohorts through ROC analysis. Calibration curves were employed to verify the accuracy of model predictions, while decision curve analysis (DCA) was applied to assess the clinical practicality of the model. Finally, a clinically applicable nomogram was constructed to facilitate model interpretation and implementation.Results A total of 153 patients were included in this study, with 42 cases (27.45%) achieving pCR. Through LASSO regression combined with univariate and multivariate logistic regression analyses, 6 key predictive features significantly associated with pCR were identified: human epidermal growth factor receptor 2 (HER2) status, Ki-67 proliferation index, pretreatment time-signal intensity curve parameters, pretreatment apparent diffusion coefficient (ADC) values, baseline MRIderived maximum tumor volume, and the rate of change in maximum tumor area between early-treatment ultrasound and pretreatment measurements. A multimodal imaging predictive model was developed by integrating these features, yielding AUC values of 0.866 (95% CI: 0.797 - 0.935) and 0.822 (95% CI: 0.702 - 0.943) for the training and validation cohorts, respectively. These findings suggested that compared with unimodal technology-based models, the multimodal imaging predictive model demonstrated superior predictive performance. Conclusion The multimodal imaging-based predictive model has been clinically validated to provide accurate probabilistic estimation of pathological complete response (pCR) attainment in breast cancer patients undergoing neoadjuvant therapy, thereby offering evidence-based decision support for personalized treatment planning and therapeutic optimization.

     

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