School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China.
Eur Radiol. 2021 Sep;31(9):6983-6991. doi: 10.1007/s00330-021-07778-0. Epub 2021 Mar 6.
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.
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).
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%).
The radiomics model based on 2-[F]fluoro-2-deoxy-D-glucose (2-[F]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.
• The clinical symptoms and imaging visual presentations of PDAC and AIP are highly similar, and accurate differentiation of PDAC and AIP lesions is difficult. • Radiomics features provided a potential noninvasive method for differentiation of AIP from PDAC. • The diagnostic performance of the proposed radiomics model indicates its potential to assist doctors in making treatment decisions.
胰腺导管腺癌（PDAC）和自身免疫性胰腺炎（AIP）是两种具有高度相似影像学表现的疾病，难以通过影像学进行区分。本研究旨在利用双时相 PET/CT 成像创建一种基于放射组学的预测模型，用于非侵入性鉴别 PDAC 和 AIP 病变。
本回顾性研究纳入了 112 例患者（48 例 AIP 和 64 例 PDAC）。所有病例均通过影像学和临床随访以及/或病理学证实。从双时相 PET/CT 图像中提取了 502 个放射组学特征，以开发放射组学决策模型。还计算了另外 12 个最大强度投影（MIP）特征，以进一步改进放射组学模型。通过支持向量机递归特征消除（SVM-RFE）选择最佳放射组学特征集，并使用线性 SVM 构建最终分类器。通过嵌套交叉验证评估所提出的双时相模型的准确性、敏感性、特异性和曲线下面积（AUC）。
最终的预测模型是通过 SVM-RFE 和线性 SVM 与所需的定量特征相结合而建立的。多模态和多维特征在分类方面表现良好（平均 AUC：0.9668，准确性：89.91%，敏感性：85.31%，特异性：96.04%）。
基于 2-[F]氟-2-脱氧-D-葡萄糖（2-[F]FDG）PET/CT 双时相图像的放射组学模型在鉴别良性 AIP 和恶性 PDAC 病变方面表现出良好的性能，这表明其有可能作为临床决策的诊断工具。
PDAC 和 AIP 的临床症状和影像学表现高度相似，准确区分 PDAC 和 AIP 病变具有一定难度。
放射组学特征为区分 AIP 和 PDAC 提供了一种潜在的非侵入性方法。
所提出的放射组学模型的诊断性能表明其有望帮助医生做出治疗决策。
