ArticleAging2020
A validated survival nomogram for early-onset diffuse gastric cancer.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Establishment and Validation of Prediction Models for Lymph Node Metastasis and Long-Term Survival in Patients with T1b Early Gastric Cancer: A Retrospective Cohort Study with 10-Year Follow-Up.Annals of surgical oncology · 2026Article
- Machine learning-based LASSO-Cox model for dementia prediction: The role of midlife cardiometabolic, inflammatory, and genetic risk factors in a US cohort.Annals of epidemiology · 2026Article
- Prognostic modeling of early-onset nondistal gastric cancer identifiesFrontiers in immunology · 2025Article
- Comparison of efficacy and safety of nab-paclitaxel and oxaliplatin + S-1 and standard S-1 and oxaliplatin chemotherapy regimens for treatment of gastric cancer.World journal of gastrointestinal surgery · 2024Article
- A prognostic nomogram based on least absolute shrinkage and selection operator Cox regression in patients with pulmonary large-cell neuroendocrine carcinoma.Translational cancer research · 2024Article
- Nomogram models for predicting overall and cancer-specific survival in early-onset gastric cancer patients: a population-based cohort study.American journal of cancer research · 2024Article
- Latin American young patients with gastric adenocarcinoma: worst prognosis and outcomes.Journal of gastrointestinal oncology · 2023Article
- Development and validation of a novel nomogram for postoperative overall survival of patients with primary gastric signet-ring cell carcinoma: a population study based on SEER database.Journal of cancer research and clinical oncology · 2023Article
- Construction of a novel signature and prediction of the immune landscape in gastric cancer based on necroptosis-related genes.Scientific reports · 2022Article
- A novel LASSO-derived prognostic model predicting survival for non-small cell lung cancer patients with M1a diseases.Cancer medicine · 2022Article
- Development and External Validation a Novel Inflammation-Based Score for Acute Kidney Injury and Prognosis in Intensive Care Unit Patients.International journal of general medicine · 2021Article
- Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients.PloS one · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to establish and independently validate a prognostic nomogram for individual risk prediction in patients with early-onset diffuse gastric cancer (EODGC). Data for 794 patients with EODGC from the SEER database were randomly assigned to training (N=558) and internal validation (N=236) sets, and data for 82 patients from the Renmin Hospital of Wuhan University (RMHWHU) were used as an independent validation cohort. Our LASSO regression analyses of the training set yielded five clinicopathological features (race, AJCC stage, surgery for primary site, chemotherapy and tumor size), which were used to create a survival nomogram. Our survival nomogram achieved better predictive performance than the AJCC staging system, the current standard. Additionally, the calibration curves of the prognostic nomogram revealed good agreement between the predicted survival probabilities and the ground truth values. Indeed, our nomogram, which estimates individualized survival probabilities for patients with EODGC, shows good predictive accuracy and calibration ability for both the SEER and RMHWHU cohorts. These results suggest that a survival nomogram may be better at predicting OS for EODGC patients than the AJCC staging system.
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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.