基于术前乳腺钼靶和MRI的瘤内及瘤周放射组学预测乳腺癌前哨淋巴结转移
Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI.
作者信息
Cheng Yuan, Xu Shu, Wang Haotian, Wang Xiaoyu, Niu Shuxian, Luo Yahong, Zhao Nannan
机构信息
Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China.
Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China.
出版信息
Front Oncol. 2022 Dec 12;12:1047572. doi: 10.3389/fonc.2022.1047572. eCollection 2022.
PURPOSE
This study aims to investigate values of intra- and peri-tumoral regions in the mammography and magnetic resonance imaging (MRI) image for prediction of sentinel lymph node metastasis (SLNM) in invasive breast cancer (BC).
METHODS
This study included 208 patients with invasive BC between Spe. 2017 and Apr. 2021. All patients underwent preoperative digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI) scans. Radiomics features were extracted from manually outlined intratumoral regions, and automatically dilated peritumoral tumor regions in each modality. The least absolute shrinkage and selection operator (LASSO) regression was used to select key features from each region to develop radiomics signatures (RSs). Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and negative predictive value (NPV) were calculated to evaluate performance of the RSs.
RESULTS
Intra- and peri-tumoral regions of BC can provide complementary information on the SLN status. In each modality, the Com-RSs derived from combined intra- and peri-tumoral regions always yielded higher AUCs than the Intra-RSs or Peri-RSs. A total of 10 and 11 features were identified as the most important predictors from mammography (DM plus DBT) and MRI (DCE-MRI plus DWI), respectively. The DCE-MRI plus DWI generated higher AUCs compared with DM plus DBT in the training (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.897 vs. 0.846) and validation (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.826 vs. 0.786) cohort.
CONCLUSIONS
Radiomics features from intra- and peri-tumoral regions can provide complementary information to identify the SLNM in both mammography and MRI. The DCE-MRI plus DWI generated lower specificity, but higher AUC, accuracy, sensitivity and negative predictive value compared with DM plus DBT.
目的
本研究旨在探讨乳腺钼靶和磁共振成像（MRI）图像中肿瘤内及肿瘤周围区域对于预测浸润性乳腺癌（BC）前哨淋巴结转移（SLNM）的价值。
方法
本研究纳入了2017年9月至2021年4月期间的208例浸润性BC患者。所有患者均接受了术前数字乳腺钼靶（DM）、数字乳腺断层合成（DBT）、动态对比增强MRI（DCE-MRI）和扩散加权MRI（DWI）扫描。从手动勾勒的肿瘤内区域以及每种模态中自动扩张的肿瘤周围区域提取影像组学特征。使用最小绝对收缩和选择算子（LASSO）回归从每个区域中选择关键特征以构建影像组学特征（RSs）。计算受试者工作特征曲线下面积（AUC）、准确性、敏感性、特异性和阴性预测值（NPV）以评估RSs的性能。
结果
BC的肿瘤内及肿瘤周围区域可为前哨淋巴结状态提供补充信息。在每种模态中，源自肿瘤内和肿瘤周围区域组合的综合RSs的AUC始终高于肿瘤内RSs或肿瘤周围RSs。分别从乳腺钼靶（DM加DBT）和MRI（DCE-MRI加DWI）中确定了10个和11个特征作为最重要的预测指标。在训练队列（AUC，DCE-MRI加DWI与DM加DBT相比，0.897对0.846）和验证队列（AUC，DCE-MRI加DWI与DM加DBT相比，0.826对0.786）中，DCE-MRI加DWI产生的AUC高于DM加DBT。
结论
肿瘤内及肿瘤周围区域的影像组学特征可为乳腺钼靶和MRI中识别SLNM提供补充信息。与DM加DBT相比，DCE-MRI加DWI特异性较低，但AUC、准确性、敏感性和阴性预测值较高。
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