Huang Yucun, Cheng Zixuan, Huang Xiaomei, Liang Cuishan, Liang Changhong, Liu Zaiyi
Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515; Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou 510080, China.
School of Medicine, South China University of Technology, Guangzhou 510006; Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou 510080, China.
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2019 Mar 28;44(3):285-289. doi: 10.11817/j.issn.1672-7347.2019.03.009.
To develop and validate a fat-suppressed (T2 weighted-magnetic resonance imaging, T2W-MRI) based radiomics signature to preoperatively evaluate the histologic grade (grade I/II VS. grade III) of invasive breast cancer. Methods: A total of 202 patients with MRI examination and pathologically confirmed invasive breast cancer from June 2011 to February 2017 were retrospectively enrolled. After retrieving fat-suppressed T2W images and tumor segmentation, radiomics features were extracted and valuable features were selected to build a radiomic signature with the least absolute shrinkage and selection operator (LASSO) method. Mann-Whitney U test was used to explore the correlation between radiomics signature and histologic grade. Receiver operating characteristics (ROC) curve was applied to determine the discriminative performance of the radiomics signature [area under curre (AUC), sensitivity, specificity, and accuracy]. An independent validation dataset was used to confirm the discriminatory power of radiomics signature. Results: Eight radiomics features were selected to build a radiomics signature, which showed good performance for preoperatively evaluating histologic grade of invasive breast cancer, with an AUC of 0.802 (95% CI 0.729 to 0.875), sensitivity of 78.7%, specificity of 70.3% and accuracy of 73.7% in training dataset and AUC of 0.812 (95% CI 0.686 to 0.938), sensitivity of 80.0%, specificity of 73.3% and accuracy of 76.0% in the validation dataset. Conclusion: The fat-suppressed T2W-MRI based radiomics signature can be used to preoperatively evaluate the histologic grade of invasive breast cancer, which may assist clinical decision-maker.
开发并验证基于脂肪抑制(T2加权磁共振成像,T2W-MRI)的影像组学特征,以术前评估浸润性乳腺癌的组织学分级(I/II级与III级)。方法:回顾性纳入2011年6月至2017年2月期间共202例接受MRI检查且病理确诊为浸润性乳腺癌的患者。在获取脂肪抑制T2W图像并进行肿瘤分割后,提取影像组学特征并选择有价值的特征,采用最小绝对收缩和选择算子(LASSO)方法构建影像组学特征。使用曼-惠特尼U检验探索影像组学特征与组织学分级之间的相关性。应用受试者操作特征(ROC)曲线确定影像组学特征的鉴别性能[曲线下面积(AUC)、敏感性、特异性和准确性]。使用独立验证数据集确认影像组学特征的鉴别能力。结果:选择8个影像组学特征构建影像组学特征,其在术前评估浸润性乳腺癌组织学分级方面表现良好,训练数据集中AUC为0.802(95%CI 0.729至0.875),敏感性为78.7%,特异性为70.3%,准确性为73.7%;验证数据集中AUC为0.812(95%CI 0.686至0.938),敏感性为80.0%,特异性为73.3%,准确性为76.0%。结论:基于脂肪抑制T-2W-MRI的影像组学特征可用于术前评估浸润性乳腺癌的组织学分级,这可能有助于临床决策者。