Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
Breast Cancer Res. 2024 May 14;26(1):77. doi: 10.1186/s13058-024-01836-3.
Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients.
The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways.
A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system.
Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.
早期预测病理完全缓解（pCR）对于决定乳腺癌患者的适当治疗策略非常重要。在这项研究中，我们旨在量化动态对比增强磁共振成像（DCE-MRI）的动态特征，并研究其提高 pCR 预测准确性的价值，以及与肿瘤异质性的相关性。
从公共数据集回顾性纳入了 785 名接受新辅助化疗的乳腺癌患者的 DCE-MRI、临床病理记录和全转录组数据。使用 22 个 CAnonical Time-sereis CHaracteristics 从提取的时变放射组学特征系列中计算 DCE-MRI 的动态特征。使用逻辑回归分别基于动态特征和传统放射组学特征开发了动态模型和放射组学模型。还开发了各种与临床因素相结合的模型，以找到最佳组合，并评估每个组成部分的意义。在独立测试集中，根据接受者操作特征曲线（AUC）评估所有模型。为了探索潜在的生物学机制，对根据动态模型分层的患者亚组进行放射基因组学分析，以识别差异表达基因（DEGs）和富集途径。
开发了一个 10 个特征的动态模型和一个 4 个特征的放射组学模型（AUC=0.688，95%CI：0.635-0.741 和 AUC=0.650，95%CI：0.595-0.705）并进行了测试（AUC=0.686，95%CI：0.594-0.778 和 AUC=0.626，95%CI：0.529-0.722），其中动态模型的 AUC 略高（训练 p=0.181，测试 p=0.222）。临床、放射组学和动态的联合模型在 pCR 预测中达到了最高的 AUC（训练：0.769，95%CI：0.722-0.816 和测试：0.762，95%CI：0.679-0.845）。与临床放射组学联合模型相比（训练 AUC=0.716，95%CI：0.665-0.767 和测试 AUC=0.695，95%CI：0.656-0.714），添加动态成分显著提高了模型性能（训练 p<0.001，测试 p=0.005）。放射基因组学分析确定了 297 个差异表达基因，包括 CXCL9、CCL18 和 HLA-DPB1，这些基因已知与乳腺癌预后或血管生成有关。基因集富集分析进一步揭示了与免疫系统相关的基因本体论术语和途径的富集。
量化了 DCE-MRI 的动态特征，并用于开发用于改善乳腺癌患者 pCR 预测的动态模型。该模型与肿瘤异质性相关，与预后相关基因表达和免疫相关途径有关。
