ArticleAmerican journal of cancer research2021
A population-based study: how to identify high-risk T1 gastric cancer patients?
Article in American journal of cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assessing Graded Histological Regression as Basis for Treatment Modification after Neoadjuvant Therapy in LAGC: An Exploratory Phase II Study.Annals of surgical oncology · 2026Article
- Influence of visceral adipose tissue on the accuracy of tumor T-staging of gastric cancer in preoperative CT.Japanese journal of radiology · 2025Article
- Construction and validation of a nomogram based on the log odds of positive lymph nodes to predict the prognosis of T1 gastric cancer.Scientific reports · 2025Article
- Establishment and validation of a prognostic nomogram for patients with renal cell carcinoma based on SEER and TCGA database.Translational cancer research · 2023Article
- Application of machine learning algorithm in predicting distant metastasis of T1 gastric cancer.Scientific reports · 2023Article
- Construction and validation of a prognostic nomogram for anal squamous cell carcinoma.Cancer medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
In T1 gastric cancer (GC), lymph nodes metastasis (LNM) is considered as a significant prognostic predictor and closely associated with following therapeutic approaches as well as distant metastasis (DM). This study aimed to not only seek risk factors of LNM and DM but also unpack the prognosis in T1 GC patients. We performed a retrospective study enrolling 5547 patients in T1 GC from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models were produced to recognize independent risk factors of LNM and DM. Cox regression analyses were performed to identify important prognostic factors of overall survival (OS). Cancer-specific cumulative incidence was plotted by cumulative incidence function. Three nomograms of LNM, DM and OS were established and validated by receiver operating characteristic (ROC) and calibration curves to evaluate discrimination and accuracy. Decision curve analysis (DCA), clinical impact curves (CIC) and subgroups based on risk scores were constructed to measure nomograms clinical utility. The area under the curve (AUC) of LNM nomogram and DM nomogram were 0.735 and 0.896, respectively. OS nomogram was constructed and the corresponding C-index was 0.797. In conclusion, our user-friendly nomograms, which aimed to predict LNM, DM and OS in T1 gastric cancer patients, have shown high efficiency of discrimination and accuracy. These useful and visual tools may have advantageous clinical utility to identify high-risk T1 gastric patients and help clinicians to draw up an individual therapeutic strategy.
Indexed as
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33948368PMC8085846What OpenQuestion holds
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