Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects

Abstract

AbstractPurposeTo clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer.MethodsA cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography‐CT (PET‐CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score‐based directed graph called “Grouped Greedy Equivalence Search” (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do‐calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten‐fold cross‐validation was used to assess the performance of the models. The prediction results were evaluated using the R‐Squared Score (R2 score).ResultsThe final causal graphical model was formed by two PET‐based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R2 = 0.251) (p < 0.05) and was able to avoid unreasonable causality that may contradict common sense.ConclusionThe GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer.