Wang Cheng, Lin Tingting, Chen Xin, Cui Wenjing, Guo Chuangen, Wang Zhongqiu, Chen Xiao
Shanghai Institute of Medical Imaging, Shanghai, China.
Department of Radiology, Zhongshan Hospital, Shanghai Medical College Fudan University, Shanghai, China.
Cancer Biomark. 2023;36(4):279-286. doi: 10.3233/CBM-220080.
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.
In this study, we aimed to show the association between the pain and the grade of PNENs.
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.
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.
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.
腹痛或背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。
在本研究中，我们旨在揭示疼痛与PNENs分级之间的关联。
本研究共纳入186例经病理确诊的PNENs患者。收集临床特征以及组织学或影像学检查结果（大小、位置、血管侵犯、局部器官侵犯和远处转移）。采用逻辑回归分析来显示疼痛与PNENs分级之间的关联。基于相关因素构建列线图以预测PNENs的高级别。采用受试者工作特征（ROC）曲线评估大小和列线图模型的诊断性能。
该队列中疼痛的发生率为30.6%（n = 57）。血管侵犯和G3级PNENs在疼痛组中更为常见（P = 0.02，P < 0.01）。疼痛组的肿瘤大小更大，PNENs高级别的发生率高于无疼痛组（p < 0.01）。年龄、疼痛和大小是G2/G3或G3级PNENs的独立危险因素。疼痛的优势比分别为3.03（95%可信区间：1.67 - 7.91）和3.32（95%可信区间：1.42 - 7.79）。构建列线图模型以预测G2/G3或G3级PNENs。列线图模型预测G2/G3级PNENs的曲线下面积（AUC）为0.84（95%可信区间，0.77 - 0.91），预测G3级PNENs的曲线下面积为0.84（95%可信区间，0.78 - 0.91）。
腹痛或背痛与PNENs分级相关。基于临床特征的列线图可能是预测PNENs分级的有力数字工具。
