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

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.

Conditional survival of elderly primary central nervous system lymphoma.

BACKGROUND: Recent studies have reported that overall survival of elderly patients with primary central nervous system lymphoma (PCNSL), who have the highest incidence of this disease, had failed to benefit from the advancements in treatment strategies over the past decades. This highlights the necessity for intensified research to guide treatment decisions for this specific patient population. METHODS: The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute (NCI) was used to extract data of elderly PCNSL patients (age ≥ 60) who were divided into training and validation groups at the ratio of 7:3, for our analysis. Conditional survival [CS(y|x)] was defined as the probability at survival additional y years given that the patient had not died of PCNSL at a specified period of time (x years) after initial diagnosis. The CS pattern of elderly PCNSL patients was analyzed. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were applied to develop a novel CS-based nomogram. RESULTS: A total of 3315 elderly patients diagnosed with CNS lymphoma between 2000 and 2019 were extracted from the SEER database, of whom 2320 patients were divided into the training group and 995 into the internal validation group. CS analysis revealed a noteworthy escalation in the 5-year survival rate among elderly PCNSL patients for every additional year of survival. The rates progressed from an initial 21-49%, 63%, and 75%, culminating in an impressive 88% and the survival improvement over time was nonlinear. The LASSO regression identified nine predictors and multivariate Cox regression was used to successfully construct the CS-based nomogram model with favorable prediction performance. CONCLUSION: CS of elderly PCNSL patients was dynamic and increased over time. Our newly-established CS-based nomogram can provide a real-time dynamic survival estimation, allowing clinicians to better guide treatment decision for these patients.

Nomogram Model for Predicting the Overall Survival of Patients With Meningiomas: a Retrospective Cohort Study.

OBJECTIVE: To identify the significant prognostic factors of overall survival (OS) for patients living with meningiomas (MMs), and establish a novel graphical nomogram and an online dynamic nomogram. METHODS: Patients diagnosed with MMs were identified retrospectively from the SEER database. The cohort was split into training (70%) and test (30%) groups randomly. Univariable and multivariable Cox models were successively used to screen the significant prognostic factors. Subsequently, the independent predictors were used as items to establish the graphic and dynamic nomogram model. To assess the accuracy of the model, a calibration curve was plotted. To assess the discrimination performance, C-index and time-dependent area under the receiver operator characteristic curve (AUC) were selected. Additionally, the decision curve was generated to evaluate the clinical net benefit of the model. RESULTS: A total of 899 patients were involved, of which 629 and 270 were split into training group and test group, respectively. Age, sex, radiotherapy, tumor size, and tumor histology were identified as the significant prognostic factors. Based on these factors, a graphical nomogram and online nomogram (Web site: https://helloshinyweb.shinyapps.io/dynamic_nomogram/) were developed. The calibration curve showed favorable consistence between predicted and actual survival rate. C-index and time-dependent AUC showed good discrimination ability, and the decision curve analysis showed positive net benefit of the model in clinical practice. CONCLUSIONS: Age of diagnosis, sex, tumor size, tumor histology, and radiotherapy were independent predictors for OS, while extent of resection had a borderline significant. A nomogram model was successfully developed and validated to dynamically predict the long-term OS for MM patients, expecting to help neurosurgeons optimize clinical management and treatment strategies.