Background: 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. Method: 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. Results: 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. Conclusion: 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.
背景：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。在治疗开始前预测对NAC的反应是评估治疗效果的有效途径。本研究旨在设计一种机器学习流程，结合临床特征和放射组学计算机断层扫描（CT）特征，预测LABC患者对NAC治疗的肿瘤反应。 方法：本研究共纳入117例LABC患者，确定了858个临床和放射组学CT特征，以预测肿瘤对NAC治疗的反应。由于特征数量多于样本数量，降维是必不可少的步骤。为此，我们提出了一种新颖的混合特征选择方法，不仅能筛选出最优特征，还能优化分类器的超参数。该混合特征选择分为两个阶段：第一阶段采用基于过滤策略的特征选择技术，利用矩阵秩定理去除所有相关和冗余特征；第二阶段应用遗传算法，并结合支持向量机（SVM）分类器。遗传算法确定了最优特征数量和最佳特征组合。通过平衡准确率、准确率、曲线下面积（AUC）和F1分数评估所提技术的性能。本研究为预测NAC反应的二分类任务，构建了三个模型：临床特征模型、放射组学CT特征模型以及临床与放射组学CT特征组合模型。 结果：本研究共纳入117例LABC患者，平均年龄为52±11岁。其中，82例患者为NAC反应组，35例为化疗无反应组。临床特征与CT放射组学特征组合模型取得了最佳性能，准确率达到0.88。 结论：结果表明，结合临床特征与CT放射组学特征是预测LABC患者对NAC治疗反应的有效方法。
肿瘤(癌症)患者之家