Development and validation of a novel nomogram for pretreatment prediction of liver metastasis in pancreatic cancer
Development and validation of a novel nomogram for pretreatment prediction of liver metastasis in pancreatic cancer
复制标题
开发和验证用于胰腺癌肝转移治疗前预测的新型列线图。
DOI：
10.1002/cam4.2930
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发表时间：
2020-02-28
期刊：
影响因子：
4
通讯作者：
Chen, Yinting
中科院分区：
文献类型：
--
作者：
Chen, Shangxiang;Chen, Shaojie;Chen, Yinting
目的影像学检查对胰腺癌肝转移的诊断价值尚不明确。我们试图开发并验证一种预测PC患者肝转移的新nomogram。方法对2001年7月至2013年12月中山大学肿瘤中心收治的604例经病理证实的PC患者进行回顾性分析。SYSUCC队列被随机分配为训练集和内部验证集。利用这两组数据，我们通过一致性指数和校准曲线推导并验证了预后模型。另外两个独立队列于2002年8月至2013年12月分别来自中山纪念医院（SYSMH, n = 335）和广东省总医院（GDGH, n = 503）进行外部验证。结果CT报告的肝转移情况、癌胚抗原（CEA）水平和分化类型是PCLM的危险因素。最终的诊断模型具有良好的校准和判别性，一致性指数为0.97，具有稳健的内部验证。SYSMH和GDGH的一致性指数为0.93，进一步从外部验证了评分诊断PCLM的能力。该模型在各队列中均比CT、CEA和分化具有更好的校准和判别能力。基于一个大型的多机构数据库和临床实践中常规观察的ct报告状态、CEA水平和肿瘤分化，我们开发并验证了一种新的预测PLCM的nomogram。
Purpose The diagnostic value of nomogram in pancreatic cancer (PC) with liver metastasis (PCLM) is still largely unknown. We sought to develop and validate a novel nomogram for the prediction of liver metastasis in patients with PC.Method About 604 pathologically confirmed PC patients from the Sun Yat-sen University Cancer Center (SYSUCC) between July, 2001 and December, 2013 were retrospectively studied. The SYSUCC cohort was randomly assigned to as the training set and internal validation set. Using these two sets, we derived and validated a prognostic model by using concordance index and calibration curves. Another two independent cohorts between August, 2002 and December, 2013 from the Sun Yat-sen Memorial Hospital (SYSMH, n = 335) and Guangdong General Hospital (GDGH, n = 503) was used for external validation.Result Computed tomography (CT) reported liver metastasis status, carcinoembryonic antigen (CEA) level and differentiation type were identified as risk factors for PCLM in the training set. The final diagnostic model demonstrated good calibration and discrimination with a concordance index of 0.97 and had a robust internal validation. The score ability to diagnose PCLM was further externally validated in SYSMH and GDGH with a concordance index of 0.93. The model showed better calibration and discrimination than CT, CEA and differentiation in each cohort.Conclusion Based on a large multi-institution database and on the routinely observed CT-reported status, CEA level and tumor differentiation in clinical practice, we developed and validated a novel nomogram to predict PLCM.