Published online Dec 7, 2018. doi: 10.3748/wjg.v24.i45.5167
Peer-review started: September 19, 2018
First decision: October 16, 2018
Revised: October 23, 2018
Accepted: November 9, 2018
Article in press: November 9, 2018
Published online: December 7, 2018
Gallbladder cancer (GBC) is a rare tumor type with dismal outcomes. With advances in medical science, GBC patients have more treatment choices in addition to surgical resection, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy. However, 5-year survival rates are surprisingly decreasing in China. Hence, screening GBC prognostic risk factors and constructing a prognostic model with high predictive accuracy and clinical utility for assessing the survival time of patients undergoing curative intent resection for GBC are of great importance.
Nomograms can integrate several independent prognostic factors for tumor patients into one model according by weighting each indicator to predict their overall survival. Compared with a single prediction indicator, this method can therefore provide more accurate and personalized prognostic information. Unfortunately, because of rare samples and ambiguous risk factors, nomograms to estimate survival time in GBC patients, especially in China, remain limited.
To establish a nomogram with easy use and high performance for predicting the survival of GBC patients undergoing radical resection in China, which will help doctors make rational decisions with respect to treatment, prognosis, and follow-up.
To select survival-related predictors, clinical parameters consisting of age, gender, jaundice, cholecystolithiasis, diabetes, body mass index (BMI), carbohydrate antigen 19-9 (CA19-9), tumor size, pathological stage, histologic grade, and surgical margins derived from 142 GBC patients after curative intent surgical resection at Peking Union Medical College Hospital (PUMCH) were incorporated into a univariate Cox regression analysis. Model selection criteria, including the likelihood ratio test, Akaike information criterion (AIC), and stepwise, forward, and backward analyses, were applied. Jaundice, CA19-9, pathological stage, and resection (R) were combined into a survival-time predictive nomogram. The predictive accuracy of the model was estimated using the concordance index (C-index). The performance of the nomogram was estimated using a calibration curve. The predictive accuracy and net benefit of the nomogram were assessed via receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA), respectively.
A nomogram consisting of jaundice, CA19-9 levels, pathological stage, and resection margin status was constructed to predict the survival time of GBC patients after curative resection. More importantly, our nomogram exhibited high predictive accuracy (C-index: 0.803; 95%CI: 0.766-0.839) and model performance (adjusted C-index: 0.797). Due to limited samples, more samples are needed to optimize model performance.
A nomogram was constructed to predict the overall survival of GBC patients who underwent radical surgery from a clinical database of GBC at PUMCH. In addition to a conventional nomogram construction strategy, continuous predictors were first converted into categorical variables after graphical assessment. Then, optimal cutoffs were selected regarding both normal references and martingale residuals. Schoenfeld residuals were analyzed with respect to ranked survival time for selected predictors, including jaundice, CA19-9 levels, pathological stage, and R, to further evaluate whether the proportional hazards assumption was valid. Finally, the predictive accuracy and clinical utility of nomogram were checked via ROC curve analysis and DCA, respectively. In summary, this study not only introduced a novel nomogram construction method to optimize model performance but also provided more detail information for clinicians to perform patient counseling, decision-making, and follow-up scheduling.
This study describes a modeling method based on a single institution for survival prediction of rare tumors. This model had high predictive accuracy and performed well after bootstrap validation and calibration. This research strategy should be widely used to construct specific nomograms according to different institutional databases, especially for rare tumors with small sample sizes of patients with some missing data.