Lee Doo-Ho, Yoon Woongchang, Lee Areum, Han Youngmin, Byun Yoonhyeong, Kang Jae Seung, Kim Hongbeom, Kwon Wooil, Suh Young-Ah, Choi Yonghwan, Namkung Junghyun, Han Sangjo, Yi Sung Gon, Heo Jin Seok, Han In Woong, Park Joon Oh, Park Joo Kyung, Kim Song Cheol, Jun Eunsung, Kang Chang Moo, Lee Woo Jin, Lee Hyeon Kook, Lee Huisong, Lee Seungyeoun, Jeong Seung-Yong, Lee Kyu Eun, Han Wonshik, Park Taesung, Jang Jin-Young
Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea.
Department of Surgery, Gachon university Gil medical center, Incheon, Korea.
J Hepatobiliary Pancreat Sci. 2023 Jan;30(1):122-132. doi: 10.1002/jhbp.986. Epub 2021 Jun 2.
BACKGROUND/PURPOSE: The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma.
Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers.
Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively.
This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.
背景/目的：本研究旨在开发一种使用多标志物组合作为胰腺导管腺癌诊断筛查工具的预测模型。
2011年1月至2019年9月收集了1991份血液样本的多中心队列，其中609份为正常样本，145份为其他癌症（结直肠癌、甲状腺癌和乳腺癌），314份为胰腺良性疾病，923份为胰腺导管腺癌。使用三种潜在生物标志物（LRG1、TTR和CA 19-9）开发了自动化多生物标志物酶联免疫吸附测定试剂盒。在训练数据集上使用逻辑回归模型，获得胰腺导管腺癌的预测值，并将结果分类为三个风险组之一：低、中、高。用于创建模型的五个协变量是性别、年龄和三种生物标志物。
参与者被分为四组，即正常组（n = 609）、其他癌症组（n = 145）、胰腺良性疾病组（n = 314）和胰腺导管腺癌组（n = 923）。正常组、其他癌症组和胰腺良性疾病组合并为非胰腺导管腺癌组（n = 1068）。阳性和阴性预测值、敏感性和特异性分别为94.12、90.40、93.81和90.86。
本研究证明了多标志物组合在区分胰腺导管腺癌与正常和良性胰腺疾病状态以及其他癌症患者方面具有显著的诊断性能。
