Development of a Bayesian Belief Network Model for personalized prognostic risk assessment in XXXX .

@entity1 @entity5 @entity3906 @entity8945 @entity694 @entity298 @entity1349
Multimodality therapy in selected @entity1 with @entity298 is gaining acceptance. Treatment-directing decision support tools are needed to individualize care and select @entity1 best suited for cytoreductive surgery +/- hyperthermic intraperitoneal chemotherapy ( @entity8945 +/- HIPEC). The purpose of this study is to develop a predictive model that could support surgical decisions in @entity1 with @entity694 . Fifty-three @entity1 were enrolled in a prospective study collecting 31 clinical-pathological, treatment-related, and outcome data. The population was characterized by disease presentation, performance status, extent of @entity3906 ( @entity3906 Index, PCI), @entity5 histology, and nodal staging. These preoperative parameters were analyzed using step-wise machine-learned Bayesian Belief Networks (BBN) to develop a predictive model for @entity1349 ( @entity1349 ) in @entity1 considered for @entity8945 +/- HIPEC. Area-under-the-curve from receiver-operating-characteristics curves of @entity1349 predictions was calculated to determine the model's positive and negative predictive value. Model structure defined three predictors of @entity1349 : severity of symptoms (performance status), PCI, and ability to undergo @entity8945 +/- HIPEC. @entity1 with PCI < 10, resectable disease, and excellent performance status who underwent @entity8945 +/- HIPEC had 89 per cent probability of survival compared with 4 per cent for those with poor performance status, PCI > 20, who were not considered surgical candidates. Cross validation of the BBN model robustly classified @entity1349 (area-under-the-curve = 0.71). The model's positive predictive value and negative predictive value are 63.3 per cent and 68.3 per cent, respectively. This exploratory study supports the utility of Bayesian classification for developing decision support tools, which assess case-specific relative risk for a given @entity1 for oncological outcomes based on clinically relevant classifiers of survival. Further prospective studies to validate the BBN model-derived prognostic assessment tool are warranted.
