Peng Shuyi, Chen Leqing, Tao Juan, Liu Jie, Zhu Wenying, Liu Huan, Yang Fan
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
Diagnostics (Basel). 2021 Nov 11;11(11):2086. doi: 10.3390/diagnostics11112086.
To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer.
A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points.
Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase.
The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.
探讨乳腺癌新辅助治疗（NAT）前的动态对比增强磁共振成像（DCE-MRI）及影像组学特征与病理完全缓解（pCR）之间的相关性。
回顾性分析2017年6月至2020年10月间经活检证实的70例乳腺浸润性癌患者（26例为病理完全缓解，44例为非病理完全缓解）。在平扫及5期动态增强序列中，每个时相共提取1037个定量影像特征。此外，将Δ特征（对比前后特征的差值）用于后续分析。采用最小绝对收缩和选择算子（LASSO）回归方法选择与pCR相关的特征，然后使用这些特征训练多个机器学习分类器，以预测给定患者的pCR概率。计算曲线下面积（AUC）、准确率、敏感性和特异性，以评估影像组学模型在五个时间点时相的预测性能。
在五个时相中，各时相预测pCR的AUC范围为0.845至0.919。最佳单时相表现为第3时相（AUC = 0.919，敏感性0.885，特异性0.864）。各时相中5个特征在pCR组和非pCR组之间存在显著差异，大多数特征在第2或第3时相达到最大值或最小值。
从治疗前DCE-MRI各时相提取的影像组学特征具有预测肿瘤反应的鉴别能力。
