Introduction

To assess pre-therapeutic MRI-based radiomic analysis to predict the pathological complete response to neoadjuvant chemotherapy (NAC) in women with early triple negative breast cancer (TN).

Materials and methods

This monocentric retrospective study included 75 TN female patients with MRI (T1-weighted, T2-weighted, diffusion-weighted and dynamic contrast enhancement images) performed before NAC. For each patient, the tumor(s) and the parenchyma were independently segmented and analyzed with radiomic analysis to extract shape, size, and texture features. Several sets of features were realized based on the 4 different sequence images. Performances of 4 classifiers (random forest, multilayer perceptron, support vector machine (SVM) with linear or quadratic kernel) were compared based on pathological complete response (defined on the excised tissues), on 100 draws with 75% as training set and 25% as test.

Results

The combination of features extracted from different MR images improved the classifier performance (more precisely, the features from T1W, T2W and DWI). The SVM with quadratic kernel showed the best performance with a mean AUC of 0.83, a sensitivity of 0.85 and a specificity of 0.75 in the test set.

Conclusion

MRI-based radiomics may be relevant to predict NAC response in TN cancer. Our results promote the use of multi-contrast MRI sources for radiomics, providing enrich source of information to enhance model generalization.

中文翻译：

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基于多对比 MRI 的放射组学预测早期三阴性乳腺癌患者对新辅助化疗的病理完全反应

介绍

评估基于治疗前 MRI 的放射组学分析，以预测早期三阴性乳腺癌 (TN) 女性对新辅助化疗 (NAC) 的病理学完全反应。

材料和方法

这项单中心回顾性研究包括 75 名 TN 女性患者，这些患者在 NAC 之前进行了 MRI（T1 加权、T2 加权、弥散加权和动态对比增强图像）。对于每位患者，肿瘤和实质被独立分割并通过放射组学分析进行分析，以提取形状、大小和纹理特征。基于4个不同的序列图像实现了几组特征。4 个分类器（随机森林、多层感知器、具有线性或二次核的支持向量机 (SVM)）的性能基于病理完全反应（在切除的组织上定义）进行比较，在 100 次抽签中，75% 作为训练集和 25%作为测试。

结果

从不同 MR 图像中提取的特征组合提高了分类器的性能（更准确地说，是来自 T1W、T2W 和 DWI 的特征）。具有二次核的 SVM 在测试集中表现出最佳性能，平均 AUC 为 0.83，灵敏度为 0.85，特异性为 0.75。

结论

基于 MRI 的放射组学可能与预测 TN 癌症中的 NAC 反应有关。我们的结果促进了多对比 MRI 源在放射组学中的使用，提供了丰富的信息源以增强模型的泛化能力。