Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis
Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis
复制标题
DOI：
10.1007/s00330-021-07778-0
复制
发表时间：
2021-03-06
影响因子：
5.9
通讯作者：
Yang, Xiaodong
中科院分区：
文献类型：
--
作者：
Liu, Zhaobang;Li, Ming;Yang, Xiaodong
目的胰腺导管腺癌(PDAC)和自身免疫性胰腺炎(AIP)是两种视觉表现高度相似的疾病，影像难以区分。这项研究的目的是创建一个基于放射组学的预测模型，使用双时间PET/CT成像对PDAC和AIP病变进行无创分类。方法对48例急性胰腺炎和合并动脉导管未闭患者的临床资料进行回顾性分析。所有病例均经影像和临床随访和/或病理证实。从双时间PET/CT图像中提取了502个放射组学特征，建立了放射组学决策模型。为了进一步改进放射组学模型，还计算了另外12个最大密度投影(MIP)特征。通过支持向量机递归特征消除算法选择最优的放射组学特征集，并利用线性支持向量机构建最终的分类器。采用嵌套交叉验证的方法对所提出的双重时间模型的准确性、敏感性、特异性和曲线下面积(AUC)进行评估。结果将支持向量机-RFE与线性支持向量机相结合，建立了具有所需定量特征的最终预测模型。多模式和多维特征分类效果较好(平均AUC值：0.9668，准确度：89.91%，敏感度：85.31%，特异度：96.04%)。结论基于2-[F-18]氟-2-脱氧-D-葡萄糖(2-[F-18]FDG)PET/CT双时间图像的放射组学模型在鉴别良性AIP和恶性PDAC病变方面具有良好的性能，可作为临床决策的诊断工具。
Objectives Pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP) are diseases with a highly analogous visual presentation that are difficult to distinguish by imaging. The purpose of this research was to create a radiomics-based prediction model using dual-time PET/CT imaging for the noninvasive classification of PDAC and AIP lesions. Methods This retrospective study was performed on 112 patients (48 patients with AIP and 64 patients with PDAC). All cases were confirmed by imaging and clinical follow-up, and/or pathology. A total of 502 radiomics features were extracted from the dual-time PET/CT images to develop a radiomics decision model. An additional 12 maximum intensity projection (MIP) features were also calculated to further improve the radiomics model. The optimal radiomics feature set was selected by support vector machine recursive feature elimination (SVM-RFE), and the final classifier was built using a linear SVM. The performance of the proposed dual-time model was evaluated using nested cross-validation for accuracy, sensitivity, specificity, and area under the curve (AUC). Results The final prediction model was developed from a combination of the SVM-RFE and linear SVM with the required quantitative features. The multimodal and multidimensional features performed well for classification (average AUC: 0.9668, accuracy: 89.91%, sensitivity: 85.31%, specificity: 96.04%). Conclusions The radiomics model based on 2-[F-18]fluoro-2-deoxy-D-glucose (2-[F-18]FDG) PET/CT dual-time images provided promising performance for discriminating between patients with benign AIP and malignant PDAC lesions, which shows its potential for use as a diagnostic tool for clinical decision-making.