Moslemi Amir, Osapoetra Laurentius Oscar, Safakish Aryan, Sannachi Lakshmanan, Alberico David, Czarnota Gregory J
Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada.
Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada.
Cancers (Basel). 2025 Aug 23;17(17):2738. doi: 10.3390/cancers17172738.
Neoadjuvant chemotherapy (NAC) is the important and effective approach to treat locally advanced breast cancer (LABC). The prediction of response to NAC prior to start is an efficient approach to obtaining perspective about the effectiveness of treatment. The objective of this study is to design a machine learning pipeline to predict tumor response to NAC treatment for patients with LABC using the combination of clinical features and radiomics computed tomography (CT) features.
A total of 858 clinical and radiomics CT features were determined for 117 patients with LABC to predict the tumor response to NAC treatment. Since the number of features is greater than the number of samples, dimensionality reduction is an indispensable step. To this end, we proposed a novel hybrid feature selection to not only select top features but also optimize the classifier hyperparameters. This hybrid feature selection has two phases. In the first phase, we applied a filter-based strategy feature selection technique using matrix rank theorem to remove all dependent and redundant features. In the second phase, we applied a genetic algorithm which coupled with the SVM classifier. The genetic algorithm determined the optimum number of features and top features. Performance of the proposed technique was assessed by balanced accuracy, accuracy, area under curve (AUC), and F1-score. This is the binary classification task to predict response to NAC. We consider three models for this study including clinical features, radiomics CT features, and a combination of clinical and radiomics CT features.
A total of 117 patients with LABC with a mean age of 52 ± 11 were studied in this study. Of these, 82 patients with LABC were the responder group (response to NAC) and 35 were the non-response group to chemotherapy. The best performance was obtained by the combination of clinical and CT radiomics features with Accuracy = 0.88.
The results indicate that the combination of clinical features and CT radiomic features is an effective approach to predict response to NAC treatment for patients with LABC.
新辅助化疗（NAC）是治疗局部晚期乳腺癌（LABC）的重要且有效的方法。在开始治疗前预测对NAC的反应是了解治疗效果的有效途径。本研究的目的是设计一种机器学习流程，利用临床特征和放射组学计算机断层扫描（CT）特征的组合来预测LABC患者对NAC治疗的肿瘤反应。
为117例LABC患者确定了总共858个临床和放射组学CT特征，以预测对NAC治疗的肿瘤反应。由于特征数量大于样本数量，降维是必不可少的一步。为此，我们提出了一种新颖的混合特征选择方法，不仅可以选择顶级特征，还可以优化分类器超参数。这种混合特征选择有两个阶段。在第一阶段，我们应用基于滤波器的策略特征选择技术，利用矩阵秩定理去除所有相关和冗余特征。在第二阶段，我们应用了与支持向量机（SVM）分类器相结合的遗传算法。遗传算法确定了最佳特征数量和顶级特征。通过平衡准确率、准确率、曲线下面积（AUC）和F1分数评估所提出技术的性能。这是一个用于预测对NAC反应的二分类任务。我们为该研究考虑了三种模型，包括临床特征、放射组学CT特征以及临床和放射组学CT特征的组合。
本研究共纳入117例LABC患者，平均年龄为52±11岁。其中，82例LABC患者为反应者组（对NAC有反应）；35例为化疗无反应组。临床和CT放射组学特征的组合表现最佳，准确率为0.88。
结果表明，临床特征和CT放射组学特征的组合是预测LABC患者对NAC治疗反应的有效方法。
