A novel prognostic time window based on conditional survival and outcomes analyses of primary liver cancer patients

Lu, Wei-Cheng; Hong, Wenxuan; Qiu, Haibo; Zhou, Zhongguo; He, Z.; Zeng, Weian; Zhong, Weiqiang; Xie, Jingdun

doi:10.1002/cam4.4762

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Our model enables us to identify high-risk patients and enhance the treatment process by providing dynamic prognosis evaluation. Furthermore, through the use of techniques like the C-index, calibration plot, ROC analysis, and DCA analysis ( 10 , 29 , 30 ), we have verified the outstanding predictive capabilities and practical value of our newly developed CS-nomogram model. As a result, the CS-based nomogram offers numerous advantages over conventional methods, particularly in terms of its ability to provide dynamic responses in survival analysis.…”

Section: Discussionmentioning

confidence: 66%

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: Discussionmentioning

confidence: 66%

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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“…Our model enables us to identify high-risk patients and enhance the treatment process by providing real-time prognosis evaluation. Furthermore, through the use of techniques like the C-index, calibration plot, ROC analysis, and DCA analysis [9,28,29], we have veri ed the outstanding predictive capabilities and practical value of our newly developed CS-nomogram model. As a result, the CS-based nomogram offers numerous advantages over conventional methods, particularly in terms of its ability to provide real-time responses in survival analysis.…”

Section: Discussionmentioning

confidence: 92%

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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A novel prognostic time window based on conditional survival and outcomes analyses of primary liver cancer patients

Hong

Qiu

et al. 2022

Cancer Medicine

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BackgroundLiver cancer is one of the most deadly and prevalent cancers. A routine follow‐up plan for liver cancer is crucial but limited. In the present study, we aimed to disclose possible risk factors and critical survival time windows for primary liver cancer.MethodsWe enrolled 692 liver cancer patients from Sun Yat‐sen University Cancer Center (SYSUCC). Univariate and multivariate logistic regression analyses of cirrhosis and recurrence were conducted. A meta‐analysis was utilized to validate an indication of creatinine (CRE) in recurrence. Conditional survival analysis was performed using the Kaplan–Meier method. The results were further verified by the SYSUCC validation cohort and Surveillance, Epidemiology, and End Results (SEER) validation cohort.ResultsOur results indicated that A/G, history of hepatitis, history of alcohol consumption and platelet (PLT) might be potential prognostic factors for cirrhosis in liver cancer patients. CRE was significantly correlated with recurrence due to various therapies, especially after transarterial embolization. Moreover, 1.5 years to 2 years may be a critical time window for deterioration in survival rate based on the conditional survival analysis.ConclusionA/G, history of hepatitis, alcohol consumption and PLT may be potential prognostic factors for cirrhosis in liver cancer patients. More attention should be focused on the renal function when treating the patients due to the significant role of CRE. 1.5 years to 2 years is a critical time window for deterioration in survival rate for liver cancer patients that contributes to determining the optimal follow‐up plan in the future.

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A novel prognostic time window based on conditional survival and outcomes analyses of primary liver cancer patients

Cited by 3 publications

References 26 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

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

A novel prognostic time window based on conditional survival and outcomes analyses of primary liver cancer patients

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