J Am Med Inform Assoc - An interactive Bayesian model for prediction of lymph node ratio and survival in pancreatic cancer patients.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground region lymph node status long use dichotom predictor clinic outcom cancer patient recent interest turn prognost util lymph node ratio lnr quantifi proport posit node examin howev statist tool joint model lnr effect cancer surviv lackingmethod data obtain nci seer cancer registri patient diagnos pancreat ductal adenocarcinoma underw radic oncolog resect novel bayesian statist approach develop appli model simultan patient true unobserv lnr status overal surviv new web develop tool employ creat interact web applic individu patient predictionresult histolog grade t m stage import predictor lnr status signific predictor surviv includ age gender marit status grade histolog t m stage tumor size radiat therapi lnr found high signific nonlinear effect surviv furthermor predict perform surviv model compar favor studi homogen patient individu predictorsconclus provid new approach tool set predict lnr surviv general applic host cancer type includ breast colon melanoma stomach method illustr develop valid model web applic predict surviv larg set pancreat cancer patient

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