ArticleTranslational cancer research2022
Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in gastric cancer with multi-organ metastases.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Prediction-model development and temporal validation for six-month cancer-specific mortality inJournal of gastrointestinal oncology · 2026Article
- Application of Survival Quilts for prognosis prediction of gastrectomy patients based on the Surveillance, Epidemiology, and End Results database and China National Cancer Center Gastric Cancer database.Journal of the National Cancer Center · 2024Article
- Artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Nomogram can be used to accurately predict the prognosis of patients and guide treatment according to the individual situation of patients. This study is to investigate the independent prognostic factors for multi-organ metastases in gastric cancer (GC) patients, and construct and validate prognostic nomograms for overall survival (OS) and cancer-specific survival (CSS). Methods: The clinical data of GC patients with multi-organ metastases from 2010 to 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors affecting the OS and CSS of the patients were screened using univariate and multivariate Cox's proportional hazards model and the Fine-Gray competing risk model. Corresponding nomogram models were constructed to predict the OS and CSS of the patients. The reliability and accuracy of the prediction model were evaluated by consistency index (C-index), area under receiver operating characteristic (ROC) curve (AUC) and calibration curve. Results: A total of 1,386 patients were included and randomly divided into a training group (972 cases) and a validation group (414 cases) in a 7:3 ratio. Cox proportional hazards analysis showed that age [P<0.001, hazard ratio (HR) =1.29 (1.11-1.49)], race (P=0.018, HR =0.79 (0.65-0.96)], metastases [P=0.036, HR =1.96 (1.05-3.67)], tumor size [P=0.045, HR =1.35 (1.01-1.82)], degree of differentiation [P=0.002, HR =1.99 (1.30-3.06)] and metastasis surgery (P=0.005, HR =0.52 (0.33-0.82)] were independent prognostic factors for OS in GC patients with multi-organ metastases. The Fine-Gray competing risk analysis showed that age [P=0.006, HR =1.23 (1.06-1.42)], histological type [P=0.037, HR =1.53 (1.03-2.27)], metastases [P=0.009, HR =2.02 (1.19-3.41)], tumor size [P=0.028, HR =1.33 (1.03-1.70)], degree of differentiation [P=0.009, HR =1.65 (1.13-2.40)] and metastasis surgery [P=0.001, HR =0.50 (0.32-0.76)] were independent prognostic factors for CSS in GC patients with multi-organ metastases. The above factors were used to construct nomogram models for predicting OS and CSS. Both C-index and AUC of the training group and the validation group showed that the models had an acceptable predictive performance. The calibration curve showed that the predicted and ideal curves fit well, indicating that the constructed models were well-calibrated. Conclusions: Using data from the SEER database, this study established and validated nomogram models for OS and CSS in GC patients with multi-organ metastases, to help clinicians formulate accurate and individualized treatment plans.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.