Development and validation of pretreatment nomogram for disease‐specific mortality in gastric cancer‐A competing risk analysis

Bando, Etsuro; Ji, Xinge; Kattan, Michael W.; Bencivenga, Maria; Manzoni, Giovanni De; Terashima, Masanori

doi:10.1002/cam4.4279

Cited by 4 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In GEA, several prognostic nomograms were published that were developed on presurgical or pathologic variables, within heterogeneous patient populations and without accounting for biological determinants. Moreover, some of them incorporate lots of covariates, thus potentially jeopardizing the practicality of these devices [16,17].…”

Section: Discussionmentioning

confidence: 99%

Development and Multicenter Validation of a Novel Immune-Inflammation-Based Nomogram to Predict Survival in Western Resectable Gastric and Gastroesophageal Junction Adenocarcinoma (GEA): The NOMOGAST

Salati

Ruvo

Giglio

et al. 2022

JCM

View full text Add to dashboard Cite

Background. More than 50% of operable GEA relapse after curative-intent resection. We aimed at externally validating a nomogram to enable a more accurate estimate of individualized risk in resected GEA. Methods. Medical records of a training cohort (TC) and a validation cohort (VC) of patients undergoing radical surgery for c/uT2-T4 and/or node-positive GEA were retrieved, and potentially interesting variables were collected. Cox proportional hazards in univariate and multivariate regressions were used to assess the effects of the prognostic factors on OS. A graphical nomogram was constructed using R software’s package Regression Modeling Strategies (ver. 5.0-1). The performance of the prognostic model was evaluated and validated. Results. The TC and VC consisted of 185 and 151 patients. ECOG:PS > 0 (p < 0.001), angioinvasion (p < 0.001), log (Neutrophil/Lymphocyte ratio) (p < 0.001), and nodal status (p = 0.016) were independent prognostic values in the TC. They were used for the construction of a nomogram estimating 3- and 5-year OS. The discriminatory ability of the model was evaluated with the c-Harrell index. A 3-tier scoring system was developed through a linear predictor grouped by 25 and 75 percentiles, strengthening the model’s good discrimination (p < 0.001). A calibration plot demonstrated a concordance between the predicted and actual survival in the TC and VC. A decision curve analysis was plotted that depicted the nomogram’s clinical utility. Conclusions. We externally validated a prognostic nomogram to predict OS in a joint independent cohort of resectable GEA; the NOMOGAST could represent a valuable tool in assisting decision-making. This tool incorporates readily available and inexpensive patient and disease characteristics as well as immune-inflammatory determinants. It is accurate, generalizable, and clinically effectivex.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Multicenter Validation of a Novel Immune-Inflammation-Based Nomogram to Predict Survival in Western Resectable Gastric and Gastroesophageal Junction Adenocarcinoma (GEA): The NOMOGAST

Salati

Ruvo

Giglio

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…However, in recent years, nomograms have shown a better accuracy in the prediction of prognosis than the TNM stage in various types of cancer. [11][12][13][14] Although there have been some nomograms in the EC field, [15][16][17] there have been no relevant studies on node-negative elderly patients with EC. Based on the SEER database, we developed and validated a nomogram that can be used to predict cancer-specific survival (CSS) at 1, 3, and 5 years in node-negative elderly EC patients.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a prognostic nomogram for predicting cancer-specific survival in lymph node-negative elderly esophageal cancer patients: A SEER-based study

et al. 2023

View full text Add to dashboard Cite

In this study, we explored the prognostic risk factors of elderly patients (≥65 years old) with lymph node-negative esophageal cancer (EC) and established a nomogram to evaluate the cancer-specific survival of patients. The surveillance, epidemiology, and end results database was used to collect data on patients diagnosed with EC. Univariate and multivariate Cox analyses were used to determine independent prognostic factors, and the nomogram for predicting cancer-specific survival of EC patients was constructed based on the independent prognostic factors obtained from the multivariate Cox analysis. To evaluate the predictive ability of the nomogram, calibration curves, concordance index (C-index), receiver operating characteristic curves, and decision curve analysis were conducted. Kaplan–Meier method was used to analyze the long-term outcomes of EC patients with different risk stratifications. A total of 3050 cases with lymph node-negative EC were randomized into the training cohort (1525) and the validation cohort (1525). Cancer-specific mortality at 1, 3, and 5 years in the entire cohort was 30.7%, 41.8%, and 59.2%, respectively. In multivariate Cox analysis, age (P < .001), marital status (P < .001), tumor size (P < .001), Tumor-node-metastasis stage (P < .001), chemotherapy (P = .011), radiotherapy (P < .001), and surgery (P < .001) were independent prognostic factors. The C-index for the training cohort was 0.740 (95% confidence interval [CI]: 0.722–0.758), and the C-index for the validation cohort was 0.738 (95% CI: 0.722–0.754). The calibration curve demonstrated the great calibration ability of the nomogram. Based on the area under the receiver operating characteristic curve, the nomogram demonstrated a higher sensitivity than the tumor-node-metastasis stage. Decision curve analysis showed the good clinical utility of the nomogram. The risk stratification system was established using the Kaplan–Meier curve and verified by the log-rank test (P < .001). The nomogram and risk stratification system can improve the accuracy of prediction to help clinicians identify high-risk patients and make treatment decisions.

show abstract