Development and external validation of a nomogram predicting overall survival for Gastric adenocarcinoma patients with radical gastrectomy

He, Zhipeng; Lin, Feng; Cheng, Tingting; Gao, Junpeng; Wang, Haoran; Zhang, Zhigong; Deng, Xiaorong

doi:10.1080/00365521.2023.2250497

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…This study collected 3,843 patients in the SEER database diagnosed with MCC between 2000 and 2019 ( 14 ). The International Classification of Diseases for Oncology (ICD O-3) site recode was MCC with histology codes 8247/3.…”

Section: Methodsmentioning

confidence: 99%

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Zhang,

Xiang,

Chen

et al. 2024

Front. Med.

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BackgroundMerkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual’s prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model.MethodsThis study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient’s survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors’ effect on overall survival and establish a novel CS-based nomogram.ResultsA total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41–50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance.ConclusionCS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

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Section: Methodsmentioning

confidence: 99%

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Zhang,

Xiang,

Chen

et al. 2024

Front. Med.

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Influence of Postoperative Insulin Resistance on Short-Term Outcomes of Radical Gastrectomy for Gastric Cancer: A Microbiome and Metabolome-Based Prospective Cohort Study

He,

Xiong,

Liu

et al. 2024

Ann Surg Oncol

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Conditional survival analysis and dynamic prediction of long-term survival in Merkel Cell Carcinoma patients

Zhang,

Xiang,

Chen

et al. 2023

Preprint

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Background: Merkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual's prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model. Methods: This study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient's survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors’ effect on overall survival and establish a novel CS-based nomogram. Results: A total of 3843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival.The rates progressed from an initial 41% to 50%, 61%, 70%, 78%, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance. Conclusion: CS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a real-time estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

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Development and external validation of a nomogram predicting overall survival for Gastric adenocarcinoma patients with radical gastrectomy

Cited by 3 publications

References 30 publications

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Influence of Postoperative Insulin Resistance on Short-Term Outcomes of Radical Gastrectomy for Gastric Cancer: A Microbiome and Metabolome-Based Prospective Cohort Study

Conditional survival analysis and dynamic prediction of long-term survival in Merkel Cell Carcinoma patients

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