Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.
Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.
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DOI：
10.3233/xst-221349
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发表时间：
2023
影响因子：
3
通讯作者：
Qiao G
中科院分区：
文献类型：
--
作者：
Zhang K;Lin J;Lin F;Wang Z;Zhang H;Zhang S;Mao N;Qiao G
新辅助化疗（NAC）已被视为局部晚期乳腺癌患者的标准治疗方法之一。之前没有研究探讨使用基于对比增强光谱乳腺摄影 (CESM) 的放射组学列线图来预测 NAC 后病理完全缓解 (pCR) 的可行性。开发并验证基于 CESM 的放射组学列线图，以预测乳腺癌 NAC 后的 pCR。共有 118 名患者入组，分为训练数据集，包括 82 名患者（21 名 pCR 和 61 名非 pCR）和 36 名患者（9 名 pCR 和 27 名非 pCR）的测试数据集。两名放射科医生在低能量和重组图像上手动分割肿瘤感兴趣区域 (ROI)，并提取放射组学特征。组内相关系数 (ICC) 用于评估 ROI 特征提取的观察者内和观察者间一致性。在训练集中，使用方差阈值、SelectKBest方法、最小绝对收缩和选择算子回归来选择最佳放射组学特征。放射组学特征是通过所选特征的线性组合计算的。开发了包含放射组学特征评分（Rad-score）和临床风险因素的放射组学列线图。使用受试者工作特征（ROC）曲线和校准曲线评估放射组学列线图的预测性能，并使用决策曲线分析（DCA）评估放射组学列线图的临床实用性。观察者内和观察者间 ICC 分别为 0.769-0.815 和 0.786-0.853。选择 13 个放射组学特征来计算 Rad 分数。包含 Rad 评分和临床风险因子的放射组学列线图显示出令人鼓舞的校准和辨别性能，训练数据集中的 ROC 曲线下面积为 0.906（95% 置信区间 (CI)：0.840–0.966），测试数据集中的 ROC 曲线下面积为 0.790（95% CI：0.554–0.952）。基于CESM的放射组学列线图对乳腺癌NAC后的pCR具有良好的预测性能；因此具有良好的临床应用前景。
Neoadjuvant chemotherapy (NAC) has been regarded as one of the standard treatments for patients with locally advanced breast cancer. No previous study has investigated the feasibility of using a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict pathological complete response (pCR) after NAC. To develop and validate a CESM-based radiomics nomogram to predict pCR after NAC in breast cancer. A total of 118 patients were enrolled, which are divided into a training dataset including 82 patients (with 21 pCR and 61 non-pCR) and a testing dataset of 36 patients (with 9 pCR and 27 non-pCR). The tumor regions of interest (ROIs) were manually segmented by two radiologists on the low-energy and recombined images and radiomics features were extracted. Intraclass correlation coefficients (ICCs) were used to assess the intra- and inter-observer agreements of ROI features extraction. In the training set, the variance threshold, SelectKBest method, and least absolute shrinkage and selection operator regression were used to select the optimal radiomics features. Radiomics signature was calculated through a linear combination of selected features. A radiomics nomogram containing radiomics signature score (Rad-score) and clinical risk factors was developed. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate prediction performance of the radiomics nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the radiomics nomogram. The intra- and inter- observer ICCs were 0.769–0.815 and 0.786–0.853, respectively. Thirteen radiomics features were selected to calculate Rad-score. The radiomics nomogram containing Rad-score and clinical risk factor showed an encouraging calibration and discrimination performance with area under the ROC curves of 0.906 (95% confidence interval (CI): 0.840–0.966) in the training dataset and 0.790 (95% CI: 0.554–0.952) in the test dataset. The CESM-based radiomics nomogram had good prediction performance for pCR after NAC in breast cancer; therefore, it has a good clinical application prospect.