DataSheet_1_An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer.pdf

IntroductionWe aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features.
MethodsUltrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve.
ResultsWe found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value.
DiscussionThe Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

引言：本研究旨在基于放射组学模型、临床病理特征与超声特征构建列线图（Nomogram），以预测乳腺癌患者接受新辅助化疗（neoadjuvant chemotherapy, NAC）后的病理完全缓解（pathological complete response, pCR）情况。
方法：本研究回顾性分析了464例接受新辅助化疗的乳腺癌患者的超声影像资料。将所有患者进一步划分为训练队列与验证队列。分别提取新辅助化疗前的放射组学特征（radiomics signatures, RS1）、新辅助化疗2个周期后的放射组学特征（RS2），以及RS2与RS1的差值特征（Delta-RS/RS1）。分别采用LASSO回归与随机森林分析进行特征筛选与模型构建。通过单因素与多因素分析，从临床病理特征、超声特征及放射组学模型中筛选出病理完全缓解的独立预测因子。基于最优放射组学模型联合临床病理与超声特征构建列线图模型。采用受试者工作特征（receiver operating characteristic, ROC）曲线评估模型的预测性能。
结果：研究发现RS2对病理完全缓解的预测性能更佳。在验证队列中，其ROC曲线下面积为0.817（95%置信区间：0.734~0.900），高于RS1与Delta-RS/RS1。基于临床病理特征、超声特征与RS2构建的列线图可精准预测病理完全缓解情况，在验证队列中的ROC曲线下面积达0.897（95%置信区间：0.866~0.929）。决策曲线分析显示，该列线图模型具备一定的临床实用价值。
讨论：基于新辅助化疗2个周期后的放射组学特征联合临床病理与超声特征构建的列线图，在预测乳腺癌新辅助化疗疗效方面具备良好性能。