基于18F-FDG PET/CT多模纹理特征的自身免疫性胰腺炎与胰腺导管腺癌鉴别方法

Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in 18F-FDG PET/CT

自身免疫性胰腺炎(AIP)是慢性胰腺炎中的一种独特亚型,其临床表现与胰腺导管腺癌(PDA)非常相似,故AIP患者经常被误诊为PDA,承受不必要的手术。18F-FDG正电子发射断层显像/X线计算机体层成像(PET/CT)检查可以同时提供胰腺形态、密度和功能代谢的综合信息,有助于对AIP和PDA进行鉴别。然而目前临床上缺乏对PET/CT图像纹理特征的分析,基于现有的诊断手段对二者进行准确鉴别依然存在困难。因此,本文基于多模纹理特征研究AIP与PDA的鉴别。本文首先采用多种特征提取算法来提取CT和PET图像内的纹理特征,然后采用Fisher准则和与支持向量机(SVM)相结合的序列前向浮动选择算法(SFFS)选择鉴别性能最优的多模特征子集,最后采用SVM分类器实现AIP与PDA的鉴别。结果表明,对病灶的纹理分析有助于实现AIP与PDA的准确鉴别。

Autoimmune pancreatitis(AIP) is a unique subtype of chronic pancreatitis, which shares many clinical presentations with pancreatic ductal adenocarcinoma(PDA). The misdiagnosis of AIP often leads to unnecessary pancreatic resection. 18 F-FDG positron emission tomography/computed tomography(PET/CT) could provide comprehensive information on the morphology, density, and functional metabolism of the pancreas at the same time. It has been proved to be a promising modality for noninvasive differentiation between AIP and PDA. However, there is a lack of clinical analysis of PET/CT image texture features. Difficulty still remains in differentiating AIP and PDA based on commonly used diagnostic methods. Therefore, this paper studied the differentiation of AIP and PDA based on multimodality texture features. We utilized multiple feature extraction algorithms to extract the texture features from CT and PET images at first. Then, the Fisher criterion and sequence forward floating selection algorithm(SFFS) combined with support vector machine(SVM) was employed to select the optimal multi-modality feature subset. Finally, the SVM classifier was used to differentiate AIP from PDA. The results prove that texture analysis of lesions helps to achieve accurate differentiation of AIP and PDA.