Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
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DOI：
10.1186/s13058-017-0846-1
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发表时间：
2017-05-18
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影响因子：
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通讯作者：
Madabhushi A
中科院分区：
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作者：
Braman NM;Etesami M;Prasanna P;Dubchuk C;Gilmore H;Tiwari P;Plecha D;Madabhushi A
在这项研究中，我们评估了乳腺癌治疗前动态增强磁共振成像（DCE-MRI）的肿瘤内和肿瘤周围区域的放射组学纹理分析预测新辅助化疗（NAC）的病理完全缓解（pCR）的能力。回顾性分析了117例接受NAC治疗的患者。在T1加权对比增强MRI扫描的瘤内和瘤周区域内，在多个阶段计算了总共99个放射组学纹理特征。特征选择用于从训练集（n = 78）内识别一组最佳pCR相关特征，然后将其用于训练多个机器学习分类器以预测给定患者的pCR可能性。然后对39名患者进行独立的分类器测试。通过三重交叉验证，在激素受体阳性和人表皮生长因子受体2阴性（HR+，HER2 −）和三阴性或HER2+（TN/HER2+）肿瘤中分别重复实验，以确定受体状态特异性分析是否可以提高分类性能。在所有患者中，使用对角线性判别分析（DLDA）分类器，组合的肿瘤内和肿瘤周围放射组学特征集在训练集中产生0.78 ± 0.030的最大AUC，在独立测试集中产生0.74。受体状态特异性特征发现和分类能够改善pCR的预测，使用DLDA在HR+、HER2 −组中产生的最大AUC为0.83 ± 0.025，使用朴素贝叶斯分类器在TN/HER2+组中产生的最大AUC为0.93 ± 0.018。在HR+、HER2 −乳腺癌中，非pCR的特征是初始造影增强期间瘤周异质性升高。然而，TN/HER2+肿瘤的最佳特征是无应答者瘤周区域内的斑点状增强模式。发现放射组学特征强烈预测pCR独立于分类器的选择，表明其作为响应预测因子的稳健性。通过结合瘤内和瘤周放射组学方法，我们可以成功地预测pCR NAC预处理乳腺DCE-MRI，无论有或没有受体状态的先验知识。此外，我们的研究结果表明，最能预测反应的放射组学特征在不同的受体亚型之间存在差异。本文的在线版本（doi：10.1186/s13058 - 017 - 0846 - 1）包含补充材料，可供授权用户使用。
In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2−) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance. Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2− breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors. Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes. The online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.