Development and validation of a novel nomogram for pretreatment prediction of liver metastasis in pancreatic cancer. (28th February 2020)

• Record Type: Journal Article
• Authors: Chen, Shangxiang; Chen, Shaojie; Lian, Guoda; Li, Yaqing; Ye, Xijiu; Zou, Jinmao; Li, Ruomeng; Tan, Ying; Li, Xuanna; Zhang, Mengfei; Huang, Chunyu; Huang, Chengzhi; Zhang, Qiubo; Huang, Kaihong; Chen, Yinting
• Is Part Of: Cancer medicine. Volume 9:Number 9(2020)
• Page Start: 2971
• Page End: 2980
• Publication Date: 2020-02-28
• Subjects: diagnosis -- liver metastasis -- nomogram -- pancreatic cancer; 616.994005
• DOI: 10.1002/cam4.2930
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: 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. Abstract : In this study, we first established and validated a novel model based on three clinical features (computer tomography, carcinoembryonic antigen and differentiated status) for liver metastasis in pancreatic cancer. As a novel complement of the conventional TNM staging system, this model exhibited improved discrimination compared with current methods for identifying the pancreatic ductal adenocarcinoma with liver metastasis. Moreover, we revealed that this model could be conveniently used to facilitate the pretreatment individualized prediction of pancreatic cancer with liver metastasis.