Conditional survival nomogram for monitoring real-time survival of young non-metastatic nasopharyngeal cancer survivors

Luo, Jianing; Hu, Xiaonan; Ge, Xiaofeng

doi:10.1007/s00432-023-04952-3

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…We then conducted a thorough assessment and validation of this CS-based nomogram and found it showed remarkable reliability and precision in real-time prediction of patient CS. Calibration plots were utilized to evaluate the model's calibration accuracy, revealing its exceptional consistency [30]. The ROC curves remained stable over a period of 3, 5, and 10 years.…”

Section: Discussionmentioning

confidence: 99%

Conditional survival nomogram for dynamic monitoring of long-term survival in non- keratinizing large cell squamous cell carcinoma

Zhang,

Xiang,

Chen

et al. 2023

Preprint

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Background: The incidence of nonkeratinizing large cell squamous cell carcinoma (NKLCSCC) continues to rise. Compared to other squamous cell carcinoma subtypes, the NKLCSCC displays lower differentiation and higher malignancy, necessitating specialized analysis and research of this disease. Therefore, the aim of our study was to develop and evaluate a novel conditional survival (CS)-based prediction model for NKLCSCC patients, with the objective of offering timely and accurate updates on survival rates. Methods: The data for patients with NKLCSCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The identified patients were randomized into the training group and the validation group, with a proportion of 7:3. The Kaplan–Meier method was used to estimate overall survival (OS). The CS rate was defined as the likelihood of a patient surviving for a specific period of time following NKLCSCC diagnosis, based on the number of years they have already survived. We firstly described the CS pattern of the NKLCSCC patients. Subsequently, a least absolute shrinkage and selection operator (LASSO) regression method with 10-fold cross-validation was employed to identify prognostic factors. A multivariate Cox regression model was used to demonstrate these predictors’ prognostic value and to develop a CS-based nomogram model. Lastly, the predictive performance of the developed model was evaluated and validated. Results: Based on the SEER database, a total of 7,252 elderly patients with NKLCSCC were identified from 2000 to 2019, with 5,076 patients allocated to the training group and 2,176 patients assigned to the validation group. Through CS analysis, we observed that these patients exhibited a remarkable improvement in 10-year survival rate with each additional year of survival. The survival rate increased from initially 56% to 62%, 70%, 74%, 79%, 82%, 86%, 90%, 93% and ultimately reached an impressive 97%. The LASSO regression analysis achieved a 10-fold cross-validation and identified 9 significant predictive factors. Then, the CS-based nomogram was successfully constructed based on these selected predictors and it could effectively stratify risk for these patients. Furthermore, this CS-based survival prediction model was successfully validated in both training and validation groups. Conclusion: This study described the CS pattern of patients with NKLCSCC, underscoring the gradual improvement in survival rates among long-term survivors over time. We have also developed the first novel CS-based nomogram model for NKLCSCC patients, which enables real-time prognostic prediction and risk stratification, facilitating personalized treatment decision-making and devising more cost-effective follow-up strategies for clinicians.

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Section: Discussionmentioning

confidence: 99%

Conditional survival nomogram for dynamic monitoring of long-term survival in non- keratinizing large cell squamous cell carcinoma

Zhang,

Xiang,

Chen

et al. 2023

Preprint

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“…In modern medicine, many studies have suggested that utilizing dynamic survival estimation may aid in the development of effective treatment plans and follow-up strategies that could benefit individuals with cancer (Qian et al 2023 ; Luo et al 2023 ; Meng et al 2023 ). However, no survival prediction tool has been developed thus far for patients with intracranial SFT/HPC.…”

Section: Introductionmentioning

confidence: 99%

Conditional survival analysis and dynamic survival prediction for intracranial solitary-fibrous tumor/hemangiopericytoma

Song,

Yang,

Cai

et al. 2024

J Cancer Res Clin Oncol

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Background As the form of World Health Organization Central Nervous System (WHO CNS) tumor classifications is updated, there is a lack of research on outcomes for intracranial combined solitary-fibrous tumor and hemangiopericytoma (SFT/HPC). This study aimed to explore conditional survival (CS) pattern and develop a survival prediction tool for intracranial SFT/HPC patients. Methods Data of intracranial SFT/HPC patients was gathered from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. The patients were split into training and validation groups at a 7:3 ratio for our analysis. CS is defined as the likelihood of surviving for a specified period of time (y years), given that the patient has survived x years after initial diagnosis. Then, we used this definition of CS to analyze the intracranial SFT/HPC patients. The least absolute shrinkage and selection operator (LASSO) regression and best subset regression (BSR) were employed to identify predictive factors. The Multivariate Cox regression analysis was applied to establish a novel CS-based nomogram, and a risk stratification system was developed using this model. Results From the SEER database, 401 patients who were diagnosed with intracranial SFT/HPC between 2000 and 2019 were identified. Among them, 280 were included in the training group and 121 were included in the internal validation group for analysis. Our study revealed that in intracranial SFT/HPC, 5-year survival rates saw significant improvement ranging from 78% at initial diagnosis to rates of 83%, 87%, 90%, and 95% with each successive year after surviving for 1–4 years. The LASSO regression and BSR identified patient age, tumor behavior, surgery and radiotherapy as predictors of CS-based nomogram development. A risk stratification system was also successfully constructed to facilitate the identification of high-risk patients. Conclusion The CS pattern of intracranial SFT/HPC patients was outlined, revealing a notable improvement in 5-year survival rates after an added period of survival. Our newly-established CS-based nomogram and risk stratification system can provide a real-time dynamic survival estimation and facilitate the identification of high-risk patients, allowing clinicians to better guide treatment decision for these patients.

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“…Although many assessment tools have been developed for prognosis prediction in MCC patients, none of them have focused on changes in prognosis over time. Moreover, the traditional nomograms do not provide dynamic prognostic information over the course of survival time, despite allowing for personalized predictions based on the clinicopathological features of patients ( 13 ). Therefore, in this study, we combined the CS analysis and nomogram model to develop a novel CS-based nomogram for dynamic estimation of MCC’s outcomes.…”

Section: Introductionmentioning

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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Conditional survival nomogram for monitoring real-time survival of young non-metastatic nasopharyngeal cancer survivors

Cited by 7 publications

References 37 publications

Conditional survival nomogram for dynamic monitoring of long-term survival in non- keratinizing large cell squamous cell carcinoma

Conditional survival nomogram for dynamic monitoring of long-term survival in non- keratinizing large cell squamous cell carcinoma

Conditional survival analysis and dynamic survival prediction for intracranial solitary-fibrous tumor/hemangiopericytoma

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

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