Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma

• Published in: World journal of gastroenterology : WJG Vol. 25; no. 37; pp. 5655 - 5666
• Main Authors: Geng, Zhi-Min, Cai, Zhi-Qiang, Zhang, Zhen, Tang, Zhao-Hui, Xue, Feng, Chen, Chen, Zhang, Dong, Li, Qi, Zhang, Rui, Li, Wen-Zhi, Wang, Lin, Si, Shu-Bin
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 07.10.2019
• Subjects: Adenocarcinoma - mortality; Adenocarcinoma - pathology; Adenocarcinoma - therapy; Adult; Aged; Aged, 80 and over; Bayes Theorem; Chemoradiotherapy, Adjuvant - methods; Chemotherapy, Adjuvant - methods; Cholecystectomy; Clinical Decision-Making - methods; Female; Gallbladder - pathology; Gallbladder - surgery; Gallbladder Neoplasms - mortality; Gallbladder Neoplasms - pathology; Gallbladder Neoplasms - therapy; Humans; Lymphatic Metastasis - pathology; Lymphatic Metastasis - therapy; Male; Middle Aged; Models, Biological; Neoplasm Staging; Patient Selection; Prognosis; Radiotherapy, Adjuvant - methods; Retrospective Studies; Retrospective Study; SEER Program - statistics & numerical data; Survival Analysis; Survival Rate; Time Factors; United States - epidemiology; Young Adult; United States; Bayesian network; Surgery; Adjuvant therapy; Prediction model; Gallbladder carcinoma
• ISSN: 1007-9327; 2219-2840
• DOI: 10.3748/wjg.v25.i37.5655

The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma (GBC) after curative resection remain unclear. To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy. Patients with curatively resected advanced gallbladder adenocarcinoma (T3 and T4) were selected from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. A survival prediction model based on Bayesian network (BN) was constructed using the tree-augmented naïve Bayes algorithm, and composite importance measures were applied to rank the influence of factors on survival. The dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:3. The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy. A total of 818 patients met the inclusion criteria. The median survival time was 9.0 mo. The accuracy of BN model was 69.67%, and the area under the curve value for the testing dataset was 77.72%. Adjuvant radiation, adjuvant chemotherapy (CTx), T stage, scope of regional lymph node surgery, and radiation sequence were ranked as the top five prognostic factors. A survival prediction table was established based on T stage, N stage, adjuvant radiotherapy (XRT), and CTx. The distribution of the survival time (>9.0 mo) was affected by different treatments with the order of adjuvant chemoradiotherapy (cXRT) > adjuvant radiation > adjuvant chemotherapy > surgery alone. For patients with node-positive disease, the larger benefit predicted by the model is adjuvant chemoradiotherapy. The survival analysis showed that there was a significant difference among the different adjuvant therapy groups (log rank, surgery alone CTx, < 0.001; surgery alone XRT, = 0.014; surgery alone cXRT, < 0.001). The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients. Adjuvant chemoradiotherapy is expected to improve the survival significantly for patients with node-positive disease.