基于多序列磁共振成像的放射组学模型用于术前评估胰腺导管腺癌中MUC4状态的初步研究
Radiomics models based on multi-sequence MRI for preoperative evaluation of MUC4 status in pancreatic ductal adenocarcinoma: a preliminary study.
作者信息
Deng Yan, Li Yong, Wu Jia-Long, Zhou Ting, Tang Meng-Yue, Chen Yong, Zuo Hou-Dong, Tang Wei, Chen Tian-Wu, Zhang Xiao-Ming
机构信息
Medical Imaging Key Laboratory of Sichuan Province and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Department of Radiology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China.
出版信息
Quant Imaging Med Surg. 2022 Nov;12(11):5129-5139. doi: 10.21037/qims-22-112.
BACKGROUND
Mucin 4 (MUC4) overexpression promotes tumorigenesis and increases the aggressiveness of pancreatic ductal adenocarcinoma (PDAC). To date, no study has reported the association between radiomics and MUC4 expression in PDAC. Thus, we aimed to explore the utility of radiomics based on multi-sequence magnetic resonance imaging (MRI) to predict the status of MUC4 expression in PDAC preoperatively.
METHODS
This retrospective study included 52 patients with PDAC who underwent MRI. The patients were divided into two groups based on MUC4 expression status. Two feature sets were extracted from the arterial and portal phases (PPs) of dynamic contrast-enhanced MRI (DCE-MRI). Univariate analysis, minimum redundancy maximum relevance (MRMR), and principal component analysis (PCA) were performed for the feature selection of each dataset, and features with a cumulative variance of 90% were selected to develop radiomics models. Clinical characteristics were gathered to develop a clinical model. The selected radiomics features and clinical characteristics were modeled by multivariable logistic regression. The combined model integrated radiomics features from different selected data sets and clinical characteristics. The classification metrics were applied to assess the discriminatory power of the models.
RESULTS
There were 22 PDACs with a high expression of MUC4 and 30 PDACs with a low expression of MUC4. The area under the receiver operating characteristic (ROC) curve (AUC) values of the arterial phase (AP) model, the PP model, and the combined model were 0.732 (0.591-0.872), 0.709 (0.569-0.849), and 0.861 (0.760-0.961), respectively. The AUC of the clinical model was 0.666 (0.600-0.682). The combined model that was constructed outperformed the AP, the PP, and the clinical models (P<0.05, although no statistical significance was observed in the combined model AP model).
CONCLUSIONS
Radiomics models based on multi-sequence MRI have the potential to predict MUC4 expression levels in PDAC.
背景
黏蛋白4（MUC4）过表达促进肿瘤发生并增加胰腺导管腺癌（PDAC）的侵袭性。迄今为止，尚无研究报道PDAC中影像组学与MUC4表达之间的关联。因此，我们旨在探讨基于多序列磁共振成像（MRI）的影像组学在术前预测PDAC中MUC4表达状态的效用。
方法
这项回顾性研究纳入了52例接受MRI检查的PDAC患者。根据MUC4表达状态将患者分为两组。从动态对比增强MRI（DCE-MRI）的动脉期和门静脉期（PPs）提取两组特征。对每个数据集进行单因素分析、最小冗余最大相关（MRMR）分析和主成分分析（PCA）以进行特征选择，并选择累积方差达90%的特征来构建影像组学模型。收集临床特征以构建临床模型。将选定的影像组学特征和临床特征通过多变量逻辑回归进行建模。联合模型整合了来自不同选定数据集的影像组学特征和临床特征。应用分类指标评估模型的鉴别能力。
结果
有22例MUC4高表达的PDAC和30例MUC4低表达的PDAC。动脉期（AP）模型、PP模型和联合模型的受试者操作特征（ROC）曲线下面积（AUC）值分别为0.732（0.591-0.872）、0.709（0.569-0.849）和0.861（0.760-0.961）。临床模型的AUC为0.666（0.600-0.682）。构建的联合模型优于AP模型、PP模型和临床模型（P<0.05，尽管联合模型与AP模型之间未观察到统计学差异）。
结论
基于多序列MRI的影像组学模型有潜力预测PDAC中的MUC4表达水平。
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