A Novel Nomogram and Risk Classification System Predicting the Cancer-Specific Survival of Patients with Initially Diagnosed Metastatic Esophageal Cancer: A SEER-Based Study

et al. 2019

Preprint

Background: Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A prognostic model for predicting the individual disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established. Objective: We aimed to establish a survival nomogram for GAC patients who receive surgery and chemotherapy. Methods: We identified 5764 GAC patients who had received surgery and chemotherapy from the SEER (Surveillance, Epidemiology, and End Results) database. Approximately 80% (n=4034) of the included patients were randomly assigned to the training set, and the remaining patients (n=1729) were assigned to the external validation set. Nomogram was established by the training set and validated by the validation set. Results: Based on the results of a multivariate analysis, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined, number of positive regional lymph nodes, sex, race, grade, derived AJCC stage, summary stage, and radiotherapy status. The C-index (Harrell's concordance index) of the model was higher than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the nomogram showed that the probability of DSS optimally corresponded to the survival rate.Integrated discrimination improvement (IDI) and categorical net reclassification improvement (NRI) showed visible improvement. IDI for 3-, 5-and 10-year DSS were 0.058, 0.059 and 0.058, respectively (P>0.05), and NRI for 3-, 5-and 10-year DSS were 0.380 (95% CI=0.316-0.470), 0.407 (95% CI=0.350-0.505), and 0.413 (95% CI=0.336-0.519), respectively. Decision curve analysis supported that the constructed nomogram was superior to the AJCC staging system. Conclusion: The proposed nomogram provides more-reliable DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.

Section: Discussionsupporting

confidence: 68%

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

et al. 2019

Preprint

“…The NRI and IDI are more sensitive indicators than the C-index, and the NRI indicated that the proposed model reclassified the risk probabilities better than did the AJCC staging system, while the IDI demonstrated the superior ability of the constructed nomogram to distinguish cases compared with the AJCC staging system. Numerous previous studies have found benefits of applying DCA (29)(30)(31)(32)(33), and the results of the current study showed that the 3-, 5-, and 10year DCA curves displayed net benefits larger than those of the AJCC staging system, both in the training set and validation set (Figure 4).…”

Section: Discussionsupporting

confidence: 63%

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

et al. 2019

Preprint

Background: Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A significant prognostic nomogram for predicting the respective disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established.Objective: We were planning to establish a survival nomogram model for GAC patients who receive surgery and chemotherapy. Methods: We identified 5764 GAC patients who had received surgery and chemotherapy from the record of Surveillance, Epidemiology, and End Results (SEER ) database. About 70% ( n =4034) of the chosen GAC patients were randomly assigned to the training set, and the rest of the included ones ( n =1729) were assigned to the external validation set. A prognostic nomogram was constructed by the training set and the predictive accuracy of it was validated by the validation set. Results: Based on the outcome of a multivariate analysis of candidate factors, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined after surgery, number of positive regional lymph nodes , sex , race, grade, derived AJCC stage, summary stage , and radiotherapy status. The C-index (Harrell’s concordance index) of the nomogram model was some larger than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the constructed nomogram displayed that the probability of DSS commendably accord with the survival rate. Integrated discrimination improvement (IDI) revealed obvious increase and categorical net reclassification improvement (NRI) showed visible enhancement. IDI for 3-, 5- and 10- year DSS were 0.058, 0.059 and 0.058, respectively ( P >0.05), and NRI for 3-, 5- and 10- year DSS were 0.380 (95% CI=0.316–0.470), 0.407 (95% CI=0.350–0.505), and 0.413 (95% CI=0.336–0.519), respectively. Decision curve analysis (DCA) proved that the constructed nomogram was preferable to the AJCC staging system. Conclusion: The constructed nomogram supplies more credible DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.

“…Moreover, in the previous studies for constructing nomograms, the size of the validation cohort was approximately one-third to half of training cohort. [10][11][12][13][14][15] Therefore, we randomly assigned 70% of the enrolled eligible patients to the training cohort (n = 692) and 30% of the enrolled eligible patients to the validation cohort (n = 296). The training cohort was used to establish the predictive model and to develop the nomogram.…”

Section: Discussionmentioning

confidence: 99%

“…To make the best use of our data for constructing the predictive model, we managed to make a higher ratio of training cohort, and we also kept a considerable number of patients for validation of our model. Moreover, in the previous studies for constructing nomograms, the size of the validation cohort was approximately one‐third to half of training cohort 10–15 . Therefore, we randomly assigned 70% of the enrolled eligible patients to the training cohort (n = 692) and 30% of the enrolled eligible patients to the validation cohort (n = 296).…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a nomogram for predicting cancer‐specific survival of surgical resected stage I‐II adenosquamous carcinoma of the lung

Journal of Surgical Oncology

et al. 2020

Objectives Primary lung adenosquamous carcinoma (ASC) is a rare cancer subtype and has a poor prognosis. The prognostic factors for resected early‐stage ASC remain unclear. We aimed to develop a nomogram to predict lung cancer‐specific survival (LCSS) of patients undergoing surgical resection for stage I‐II ASC. Methods Data of patients undergoing resection for stage I‐II ASC and diagnosed between 2004‐2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. All the included patients were randomized at a 7:3 ratio into a training and a validation cohort. We selected and integrated significant prognostic factors based on competing for risk regression to build a nomogram. The performance of the nomogram was evaluated using Harrell's concordance index (C‐index) and calibration plots. Results A total of 988 patients (530 men and 458 women) undergoing surgical resection for stage I‐II ASC were identified and randomized into a training (692, 70%) cohort and a validation cohort (296, 30%). The baseline characteristics were similar in the training and validation cohorts. Age, T stage, N stage, and the number of examined lymph nodes were independent prognostic factors for LCSS and were used in the nomogram. The calibration plots showed that the 3‐ and 5‐year LCSS probabilities were consistent between the nomogram prediction and the actual observation. The C‐index of the nomogram was 0.671 (95%CI: 0.618‐0.724) and 0.635 (95%CI: 0.557‐0.713) in the training cohort and validation cohort, respectively. We developed a risk classification system based on the nomogram to stratify patients into high‐ and low‐risk of cancer‐specific death groups. Patients with a similar risk shared similar prognostic prediction regardless of the stage category and patients with the same risk shared similar prognoses despite the different stage category. Conclusions We developed a competing risk nomogram to reliably predict cancer‐specific survival of patients undergoing surgical resection for stage I‐II ASC. The nomogram might be a useful tool to identify patients undergoing surgical resection for ASC who could be suitable candidates for adjuvant chemotherapy.