Department of Biochemistry, Genetics and Immunology, Faculty of Biology, Universidad de Vigo. 36310 Vigo, Spain.
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center. 2300RC Leiden, The Netherlands.
Sci Rep. 2017 Jan 24;7:41151. doi: 10.1038/srep41151.
While evidence for lung cancer screening implementation in Europe is awaited, Rapid Diagnostic Units have been established in many hospitals to accelerate the early diagnosis of lung cancer. We seek to develop an algorithm to detect lung cancer in a symptomatic population attending such unit, based on a sensitive serum marker panel. Serum concentrations of Epidermal Growth Factor, sCD26, Calprotectin, Matrix Metalloproteinases -1, -7, -9, CEA and CYFRA 21.1 were determined in 140 patients with respiratory symptoms (lung cancer and controls with/without benign pathology). Logistic Lasso regression was performed to derive a lung cancer prediction model, and the resulting algorithm was tested in a validation set. A classification rule based on EGF, sCD26, Calprotectin and CEA was established, able to reasonably discriminate lung cancer with 97% sensitivity and 43% specificity in the training set, and 91.7% sensitivity and 45.4% specificity in the validation set. Overall, the panel identified with high sensitivity stage I non-small cell lung cancer (94.7%) and 100% small-cell lung cancers. Our study provides a sensitive 4-marker classification algorithm for lung cancer detection to aid in the management of suspicious lung cancer patients in the context of Rapid Diagnostic Units.
虽然欧洲正在等待肺癌筛查实施的证据，但许多医院已经建立了快速诊断单位，以加速肺癌的早期诊断。我们旨在开发一种算法，以根据敏感的血清标志物面板检测在该单位就诊的有症状人群中的肺癌。我们测定了 140 例有呼吸道症状的患者（肺癌和有/无良性病变的对照组）的血清表皮生长因子、可溶性 CD26、钙卫蛋白、基质金属蛋白酶-1、-7、-9、CEA 和 CYFRA 21.1 的浓度。进行逻辑套索回归以得出肺癌预测模型，并在验证集中测试该算法。建立了一种基于 EGF、sCD26、Calprotectin 和 CEA 的分类规则，能够在训练集中以 97%的敏感性和 43%的特异性合理地区分肺癌，在验证集中的敏感性和特异性分别为 91.7%和 45.4%。总体而言，该面板能够以 94.7%的敏感性识别出 I 期非小细胞肺癌，100%的小细胞肺癌。我们的研究提供了一种用于肺癌检测的敏感 4 标志物分类算法，以帮助快速诊断单位中可疑肺癌患者的管理。
