The association between pain and WHO grade of pancreatic neuroendocrine neoplasms: A multicenter study.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Cheng Wang, Tingting Lin, Xin Chen, Wenjing Cui, Chuangen Guo, Zhongqiu Wang, Xiao Chen
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引用次数: 0

Abstract

Background: Abdominal or back pain is a common symptom in pancreatic diseases. However, the role of pain in pancreatic neuroendocrine neoplasm (PNENs) has not been clarified.

Objective: In this study, we aimed to show the association between the pain and the grade of PNENs.

Methods: A total of 186 patients with pathologically confirmed PNENs were included in this study. Clinical features and histological or radiological findings (size, location, and vascular invasion and local organs invasion and distal metastasis) were collected. Logistic regression analyses were used to show the association between pain and grade of PNENs. Nomogram was developed based on associated factors to predict the higher grade of PNENs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of size and nomogram model.

Results: The prevalence of pain in the cohort was 30.6% (n= 57). The vascular invasion and G3 PNENs were more common in the pain group (P= 0.02, P< 0.01). The tumor size was larger and incident of higher grade of PNENs was higher in the pain group than the non-pain group (p< 0.01). Age, pain and size were independent risk factors for G2/G3 or G3 PNENs. The odds ratio was 3.03 (95% CI: 1.67-7.91) and 3.32 (95% CI: 1.42-7.79) for pain, respectively. The nomogram model was developed to predict the G2/G3 or G3 PNENs. The area under the curve (AUC) of the nomogram model was 0.84 (95% CI, 0.77-0.91) in predicting the G2/G3 PNENs, and was 0.84 (95% CI, 0.78-0.91) in predicting the G3 PNENs.

Conclusion: Abdominal or back pain is associated with the grade of PNENs. The nomograms based on clinical features may be a powerful numerical tool for predicting the grade of PNENs.

疼痛与胰腺神经内分泌肿瘤WHO分级的关系:一项多中心研究。
背景:腹部或背部疼痛是胰腺疾病的常见症状。然而,疼痛在胰腺神经内分泌肿瘤(PNENs)中的作用尚未明确。目的:在本研究中,我们旨在揭示疼痛与PNENs分级之间的关系。方法:对186例经病理证实的PNENs患者进行研究。收集临床特征和组织学或放射学表现(大小、位置、血管侵犯、局部器官侵犯和远端转移)。Logistic回归分析显示疼痛与PNENs分级之间的关系。基于相关因素建立Nomogram预测PNENs的分级。采用受试者工作特征(ROC)曲线评价大小和nomogram模型的诊断性能。结果:队列中疼痛的患病率为30.6% (n= 57)。疼痛组血管侵及G3 PNENs发生率较高(P= 0.02, P< 0.01)。疼痛组肿瘤体积较大,PNENs高分级发生率高于非疼痛组(p< 0.01)。年龄、疼痛和大小是G2/G3或G3 PNENs的独立危险因素。疼痛的优势比分别为3.03 (95% CI: 1.67-7.91)和3.32 (95% CI: 1.42-7.79)。采用nomogram模型预测G2/G3或G3 PNENs。图模型预测G2/G3 PNENs的曲线下面积(AUC)为0.84 (95% CI, 0.77 ~ 0.91),预测G3 PNENs的曲线下面积(AUC)为0.84 (95% CI, 0.78 ~ 0.91)。结论:腹部或背部疼痛与PNENs的分级有关。基于临床特征的形态图可能是预测PNENs分级的有力数值工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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