Conditional survival analysis and real-time prognosis prediction for cervical cancer patients below the age of 65 years

Meng, Xiangdi; Jiang, Yingxiao; Chang, Xiaolong; Zhang, Yan; Guo, Yinghua

doi:10.3389/fonc.2022.1049531

Cited by 5 publications

(5 citation statements)

References 32 publications

(43 reference statements)

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“…In this study, we utilized the least absolute shrinkage and selection operator (LASSO) regression technique with 10-fold cross-validation to identify independently prognostic factors in the training cohort [16]. Subsequently, multivariate Cox regression was conducted to con rm the prognostic value of the selected variables and integrate them into a new nomogram model [17]. The CS concept was nally utilized in the development of a CS-nomogram, which is capable of providing personalized, real-time prognostic information that is continually updated.…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, we computed the total count and percentage of categorical variables in the entire cohort, training cohort, and validation cohort. CS(y|x) is the probability of additional y years of survival given that the patient has not died of MCC by a speci c period of time (x years) after initial diagnosis [14]. Standard de nition of conditional probability was used to calculate CS: CS(y|x) = OS(y + x)/OS(x).…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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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“…The ROC curves remained stable over a period of 3, 5, and 10 years. Finally, we evaluated the e cacy of this model in the healthcare system, and our survival prognostication model demonstrated superior cumulative bene t and greater robustness to survival probabilities via DCA analysis [31,32]. This indicated that our model demonstrated outstanding predictive performance and clinical applicability, providing patients with greater overall bene ts and aiding physicians in making treatment decisions.…”

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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“…In this study, we utilized the least absolute shrinkage and selection operator (LASSO) regression technique with 10-fold cross-validation to identify independently prognostic factors in the training cohort ( 17 ). Subsequently, multivariate Cox regression was conducted to confirm the prognostic value of the selected variables and integrate them into a new nomogram model ( 18 ). The CS concept was finally utilized in the development of a CS-nomogram, which is capable of providing personalized, dynamic prognostic information that is continually updated.…”

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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Conditional survival analysis and real-time prognosis prediction for cervical cancer patients below the age of 65 years

Cited by 5 publications

References 32 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 nomogram for dynamic monitoring of long-term survival in non- keratinizing large cell squamous cell carcinoma

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

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