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

Abstract

BACKGROUNDThe factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma (GBC) after curative resection remain unclear.AIMTo provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.METHODSPatients 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.RESULTSA 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).CONCLUSIONThe 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.

Validation of the 8th Edition American Joint Commission on Cancer (AJCC) Gallbladder Cancer Staging System: Prognostic Discrimination and Identification of Key Predictive Factors

“…The recently published BILCAP trial, of which 17.7% of the total trial population had GBC, demonstrated that adjuvant chemotherapy improves survival in surgically resected patients by 17 months [ 22 ]. The beneficial effect of radiation on the survival of GBC patients seen in our data is supported by some single institution studies [ 36 , 37 , 38 , 39 , 40 ], while previous SEER studies have reported conflicting results [ 34 , 41 ]. This is of clinical relevance and warrants further investigation regarding the role of radiation therapy in the management of GBC.…”

Clinicopathological characteristics and prognosis of signet ring cell carcinoma of the gallbladder

“…Given the poor prognosis of gallbladder SRC, it is necessary to find an optimal treatment strategy. Total tumor excision with adjuvant chemoradiotherapy is the mainstay of treatment for gallbladder adenocarcinomas at present [ 27 , 28 ]. However, no standardized protocol and guideline for the treatment of gallbladder SRC are available at present because of the limited number of cases and studies.…”

Identification of patients who may benefit the most from adjuvant chemotherapy following resection of incidental gallbladder carcinoma

“…Few studies have established prediction models to identify patients who might benefit from adjuvant chemotherapy after resection of GBC 29‐32 . Wang et al 15 published a web‐based nomogram that predicted subsets of patients with at least T2 or N1 disease would benefit from adjuvant chemoradiotherapy.…”

“…Wang et al 15 published a web‐based nomogram that predicted subsets of patients with at least T2 or N1 disease would benefit from adjuvant chemoradiotherapy. Similarly, Geng et al 29 reported a survival benefit estimation model of adjuvant chemoradiotherapy based on a Bayesian network using the Surveillance, Epidemiology, and End Results (SEER) database. Building on this previous work, the current study was the first to establish a scoring system based on a broader range of five well‐established (and accessible) risk factors to define the survival benefit of adjuvant chemotherapy among patients with IGBC.…”