World journal of gastroenterology · 2019
Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma.
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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
Аннотация
BACKGROUND: The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma (GBC) after curative resection remain unclear.
AIM: To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.
METHODS: 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.
RESULTS: 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 vs CTx, P < 0.001; surgery alone vs XRT, P = 0.014; surgery alone vs cXRT, P < 0.001).
CONCLUSION: 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.
AIM: To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.
METHODS: 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.
RESULTS: 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 vs CTx, P < 0.001; surgery alone vs XRT, P = 0.014; surgery alone vs cXRT, P < 0.001).
CONCLUSION: 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.
Предметные рубрики MeSH
AdenocarcinomaAdultAgedAged, 80 and overBayes TheoremChemoradiotherapy, AdjuvantChemotherapy, AdjuvantCholecystectomyClinical Decision-MakingFemaleGallbladderGallbladder NeoplasmsHumansLymphatic MetastasisMaleMiddle AgedModels, BiologicalNeoplasm StagingPatient SelectionPrognosisRadiotherapy, AdjuvantRetrospective StudiesSEER ProgramSurvival AnalysisSurvival RateTime FactorsUnited StatesYoung Adult