An interactive Bayesian model for prediction of lymph node ratio and survival in pancreatic cancer patients.
BACKGROUND
Regional lymph node status has long been used as a dichotomous predictor of clinical outcomes in cancer patients. More recently, interest has turned to the prognostic utility of lymph node ratio (LNR), quantified as the proportion of positive nodes examined. However, statistical tools for the joint modeling of LNR and its effect on cancer survival are lacking.


METHODS
Data were obtained from the NCI SEER cancer registry on 6400 patients diagnosed with pancreatic ductal adenocarcinoma from 2004 to 2010 and who underwent radical oncologic resection. A novel Bayesian statistical approach was developed and applied to model simultaneously patients' true, but unobservable, LNR statuses and overall survival. New web development tools were then employed to create an interactive web application for individualized patient prediction.


RESULTS
Histologic grade and T and M stages were important predictors of LNR status. Significant predictors of survival included age, gender, marital status, grade, histology, T and M stages, tumor size, and radiation therapy. LNR was found to have a highly significant, non-linear effect on survival. Furthermore, predictive performance of the survival model compared favorably to those from studies with more homogeneous patients and individualized predictors.


CONCLUSIONS
We provide a new approach and tool set for the prediction of LNR and survival that are generally applicable to a host of cancer types, including breast, colon, melanoma, and stomach. Our methods are illustrated with the development of a validated model and web applications for the prediction of survival in a large set of pancreatic cancer patients.
Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USA.
Brian J
BJ
Smith
Division of Surgical Oncology and Endocrine Surgery, Department of Surgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.
James J
JJ
Mezhir
D001499:Bayes Theorem; D021441:Carcinoma, Pancreatic Ductal / Q000401:mortality / Q000473:pathology*; D006801:Humans; D020407:Internet; D016015:Logistic Models; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D010190:Pancreatic Neoplasms / Q000401:mortality / Q000473:pathology*; D011379:Prognosis; D018426:SEER Program; D016019:Survival Analysis
Bayesian Prediction; Biostatistics; Medical Informatics; Pancreas Cancer; SEER; Survival Analysis