在我国,胰腺是神经内分泌肿瘤最为常见的发生部位。胰腺神经内分泌肿瘤(pancreatic neuroendocrine tumors,简称pNETs)是一类起源于胰腺多能神经内分泌干细胞的肿瘤,在所有胰腺肿瘤中所占的比例大约为1%~2%。随着公众健康意识的日益增长以及检测技术的不断进步,pNETs的发病率和检出率都在逐年升高,但针对pNETs的治疗目前仍以手术治疗为主要手段,并且根治性手术切除被公认为唯一可能彻底治愈pNETs的方法;但令人痛惜的是,很多患者在确诊时已进展至中晚期,已错过了行根治性手术的机会;幸运的是,目前结合其他化疗、放疗、免疫等非手术治疗,疗效也大幅提升。但提高对该病的认识,进行科学的“排兵布阵”,实行精准的个体化治疗,才更有利于提高该疾病的生存期。为了更精准地制定个体化治疗方案,本文就近年来胰腺神经内分泌肿瘤的治疗进展做一概述。In China, the pancreas is the most common site of neuroendocrine tumors. Pancreatic neuroendocrine tumors (PNETs) are a class of tumors originating from pancreatic pluripotent neuroendocrine stem cells, accounting for about 1% to 2% of all pancreatic tumors. With the growing awareness of public health and the continuous progress of detection technology, the incidence and detection rates of PNETs are increasing year by year. However, surgical treatment is still the main treatment for PNETs, and radical surgical resection is recognized as the only method that can completely cure PNETs;However, it is regrettable that many patients have progressed to the middle and late stage at the time of diagnosis, and have missed the opportunity of radical surgery. Fortunately, the curative effect has also been greatly improved in combination with other non-surgical treatments such as chemotherapy, radiotherapy and immunization. However, to improve the understanding of the disease, carry out scientific “troop arrangement”, and implement accurate and indiv
胰腺神经内分泌肿瘤(neuroendocrine tumors of pancreas,pNET)是一种特殊类型的胰腺肿瘤,发病率低,但近年来有明显上升趋势。pNET总体治疗效果好,但pNET合并血管侵犯及静脉瘤栓的诊治经验鲜有报道。本文报告1例患者的诊治历程,首次诊断时胰腺肿瘤巨大,并有脾静脉瘤栓,经积极手术后明确为pNET。从首诊至今已经24年,患者在此期间有肿瘤复发再手术,也有颈椎炎性病变及白血病等新发病症,总体治疗效果满意。
目的探究胰腺神经内分泌肿瘤(pancreatic neuroendocrine neoplasm,panNEN)的CT和MRI特征对预测其病理分级的价值。材料与方法回顾性分析北京大学第三医院106例panNEN患者的临床及影像资料,本研究遵循世界卫生组织(World Health Organization,WHO)2019年第五版的分类和分级标准,将panNEN中的G1、G2、G3级神经内分泌肿瘤(neuroendocrine neoplasm,NEN)和神经内分泌癌(neuroendocrine carcinoma,NEC)分别划分为低级别组(G1级NEN)和中高级别组(包括G2、G3级NEN和NEC)。对患者性别、年龄和病灶的形态、位置、体积、囊实性质、CT特征(平扫、增强动脉期和静脉期相CT值、动脉期和静脉期CT图像的增强模式)、MRI特征[T1、T2加权MRI图像上的信号强度、扩散加权成像(diffusion-weighted imaging,DWI)序列b值=1000 s/mm^(2)图像的信号强度及表观扩散系数(apparent diffusion coefficient,ADC)图像的信号强度],以及血管侵犯和肝转移进行统计学分析。运用t检验、Mann-Whitney U检验、卡方检验及Wilcoxon秩和检验比较panNEN不同病理分级和病灶相关参数的差异,并采用二元logistic回归构建预测模型,使用受试者工作特征曲线下面积(area under the curve,AUC)评估模型预测效能,采用DeLong检验比较模型间的AUC值的差异。校准曲线评估模型的拟合度,决策曲线分析评估模型的临床价值。结果低级别组与中高级别组在肿瘤体积、肝转移和血管侵犯方面的差异具有统计学意义(P<0.05),而在性别、年龄、囊实性质和发生部位方面的差异无统计学意义(P>0.05)。CT和MRI特征中,仅DWI和ADC图信号特征差异具有统计学意义。多因素logistic回归分析显示,肿瘤体积、肝转移和血管侵犯是panNEN病理分级的独立预测因素,联合后构建的模型预测panNEN中高级别组的AUC达0.861(95%CI:0.798~0.923),敏感度为78.1%,特异度为83.3%。结论基于肿瘤体积、肝转移和血管侵犯的联合模型在术前能有�
