Xu Jian-Xia, Hu Jin-Bao, Yang Xiao-Yan, Feng Na, Huang Xiao-Shan, Zheng Xiao-Zhong, Rao Qin-Pan, Wei Yu-Guo, Yu Ri-Sheng
Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Front Oncol. 2023 Jan 16;12:1106525. doi: 10.3389/fonc.2022.1106525. eCollection 2022.
To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC) preoperatively.
A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination.
Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort.
We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.
探讨小细胞肺癌胰腺转移（PM-SCLC）的临床特征、影像学特征及生物标志物，并建立一种便捷的列线图诊断预测模型，以在术前鉴别PM-SCLC与胰腺导管腺癌（PDAC）。
回顾性分析2016年1月至2022年3月共299例符合标准的患者（PM-SCLC 93例；PDAC 206例），其中包括来自医院1的249例患者（训练/内部验证队列）和来自医院2的50例患者（外部验证队列）。我们寻找有意义的临床特征、影像学特征和生物标志物，并通过多变量逻辑回归分析确定预测因素。为PM-SCLC的诊断和预测建立了三个模型：临床模型、CT影像模型和联合模型。基于独立预测因素构建列线图。采用受试者工作特征曲线评估鉴别能力。
多变量逻辑回归分析中用于PM-SCLC诊断的六个独立预测因素，包括临床症状、CA199、肿瘤大小、实质萎缩、血管受累和强化类型。基于这六个独立预测因素的列线图诊断预测模型表现最佳，训练队列（n = 174）、内部验证队列（n = 75）和外部验证队列（n = 50）的曲线下面积（AUC）分别为0.950（95%可信区间，0.917 - 0.976）、0.928（95%可信区间，0.873 - 0.971）和0.976（95%可信区间，0.944 - 1.00）。该模型在训练队列中的敏感性为94.50%、特异性为83.20%、准确性为86.80%；在内部验证队列中的敏感性为100.00%、特异性为80.40%、准确性为86.70%；在外部验证队列中的敏感性为100.00%、特异性为88.90%、准确性为87.50%。
我们提出了一种基于临床特征、影像学特征和生物标志物的无创且便捷的列线图诊断预测模型，用于术前鉴别PM-SCLC与PDAC。
