Liu Jinhui, Leng Xiaoling, Liu Wen, Ma Yuexin, Qiu Lin, Zumureti Tuerhong, Zhang Haijian, Mila Yeerlan
Department of Ultrasound, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China.
Artificial Intelligence and Smart Mine Engineering Technology Center, Xinjiang Institute of Engineering, Urumqi, China.
Front Oncol. 2024 Mar 4;14:1285511. doi: 10.3389/fonc.2024.1285511. eCollection 2024.
We 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.
Ultrasound 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.
We 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.
The 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.
我们旨在通过构建基于影像组学模型、临床病理特征和超声特征的列线图，预测乳腺癌患者新辅助化疗（NAC）的病理完全缓解（pCR）情况。
回顾性分析464例接受NAC的乳腺癌患者的超声图像。患者进一步分为训练队列和验证队列。获取NAC治疗前的影像组学特征（RS）（RS1）、NAC两个周期后的影像组学特征（RS2）以及RS2与RS1之间的差异特征（Delta-RS/RS1）。分别采用LASSO回归和随机森林分析进行特征筛选和模型构建。通过单因素和多因素分析从临床病理特征、超声特征和影像组学模型中筛选pCR的独立预测因素。基于最佳影像组学模型以及临床病理和超声特征构建列线图模型。采用受试者操作特征（ROC）曲线评估预测性能。
我们发现RS2对pCR具有更好的预测性能。在验证队列中，ROC曲线下面积为0.817（95%CI：0.734 - 0.900），高于RS1和Delta-RS/RS1。基于临床病理特征、超声特征和RS2构建的列线图能够准确预测pCR值，在验证队列中的ROC曲线下面积为0.897（95%CI：0.866 - 0.929）。决策曲线分析表明列线图模型具有一定的临床实用价值。
基于NAC两个周期后的影像组学特征以及临床病理和超声特征构建的列线图在预测乳腺癌NAC疗效方面具有良好性能。
