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

Jin Zhang; Yang Xiang; Jiqiu Chen; Lei Liu; Jian Jin; Shihui Zhu

doi:10.3389/fmed.2024.1354439

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

Front Med (Lausanne). 2024 Feb 8:11:1354439. doi: 10.3389/fmed.2024.1354439. eCollection 2024.

Authors

Jin Zhang^#^{1

2}, Yang Xiang^#¹, Jiqiu Chen¹, Lei Liu^{1

2}, Jian Jin¹, Shihui Zhu^{1

2}

Affiliations

¹ The First Affiliated Hospital of the Naval Medical University, Shanghai, China.
² Shanghai Children's Medical Center, School of Medicine, Shanghai Jiaotong University, Shanghai, China.

^# Contributed equally.

Abstract

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 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.

Conclusion: CS 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.

Keywords: MCC; Merkel cell carcinoma; SEER; conditional survival; nomogram; overall survival.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Development of Standardized Clinical Trials and Establishment of Follow-up Evaluation System for in situ Regeneration of Intelligent Biomaterials (2019YFA0110603) and Key Research Project “Research on Key Technologies and Equipment for Early Treatment of Naval Battle Injuries in 2019” (2019YSL010).