Development and Validation of Prognostic Nomogram in Patients With WHO Grade III Meningioma: A Retrospective Cohort Study Based on SEER Database

Survival differences in malignant meningiomas: a latent class analysis using SEER data

BACKGROUND

Several studies have used demographic characteristics to examine differences in survival time for patients with malignant meningioma (MM). Latent class analysis (LCA), with its ability to identify mutually patterns of patients in a heterogeneous population. The aim of our study was to analyze the heterogeneity of sociodemographic characteristics in meningioma.

METHODS

The data of patients diagnosed with malignant meningioma (n = 1,562, age > 18 years old) were extracted from the Surveillance, Epidemiology, and End Result database. Data on sociodemographic characteristics such as age, sex, race, NHIA, marital status, household income, rural or urban residential area, and overall survival time were included. LCA was used to identify heterogeneous patterns of MM. each group was explored using Bayesian network analysis.

RESULTS

In total, 1562 patients with MM were processed by the LCA model; the 4-class latent class models were the best fit. LCA identified four survival groups: highest, intermediate-high, low-to-moderate, and lowest survival groups. Patients with the longest survival times-93.59 months-were 40-59 years old, female, Black, non-Hispanic, married, and had a family income of $60,000-$74,999 and lived in densely populated areas. Bayesian networks revealed correlations between patients with MM and sociodemographic characteristics in different latent class groups.

CONCLUSION

We identified and verified differences in clinical and sociodemographic characteristics between survival groups. A comprehensive understanding of the "people-oriented" subgroup characteristics will greatly benefit the diagnosis and treatment of MM.

MRI features and tumor-infiltrating CD8 + T cells-based nomogram for predicting meningioma recurrence risk

OBJECTIVE

This study was based on MRI features and number of tumor-infiltrating CD8 + T cells in post-operative pathology, in predicting meningioma recurrence risk.

METHODS

Clinical, pathological, and imaging data of 102 patients with surgically and pathologically confirmed meningiomas were retrospectively analyzed. Patients were divided into recurrence and non-recurrence groups based on follow-up. Tumor-infiltrating CD8 + T cells in tissue samples were quantitatively assessed with immunohistochemical staining. Apparent diffusion coefficient (ADC) histogram parameters from preoperative MRI were quantified in MaZda. Considering the high correlation between ADC histogram parameters, we only chose ADC histogram parameter that had the best predictive efficacy for COX regression analysis further. A visual nomogram was then constructed and the recurrence probability at 1- and 2-years was determined. Finally, subgroup analysis was performed with the nomogram.

RESULTS

The risk factors for meningioma recurrence were ADCp1 (hazard ratio [HR] = 0.961, 95% confidence interval [95% CI]: 0.937 ~ 0.986, p = 0.002) and CD8 + T cells (HR = 0.026, 95%CI: 0.001 ~ 0.609, p = 0.023). The resultant nomogram had AUC values of 0.779 and 0.784 for 1- and 2-years predicted recurrence rates, respectively. The survival analysis revealed that patients with low CD8 + T cells counts or ADCp1 had higher recurrence rates than those with high CD8 + T cells counts or ADCp1. Subgroup analysis revealed that the AUC of nomogram for predicting 1-year and 2-year recurrence of WHO grade 1 and WHO grade 2 meningiomas was 0.872 (0.652) and 0.828 (0.751), respectively.

CONCLUSIONS

Preoperative ADC histogram parameters and tumor-infiltrating CD8 + T cells may be potential biomarkers in predicting meningioma recurrence risk.

CLINICAL RELEVANCE STATEMENT

The findings will improve prognostic accuracy for patients with meningioma and potentially allow for targeted treatment of individuals who have the recurrent form.

Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging

Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.

Development and validation of a prognostic nomogram for patients with ganglioneuroblastoma: A SEER-based study

Background

The objective of this study was to construct a prognostic nomogram for ganglioneuroblastoma (GNB), as the prognosis of GNB is difficult to accurately predict before therapy.

Methods

The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients included in this study were randomly divided into a development group and a validation group at a ratio of 7:3. Univariate and multivariate Cox regression analyses were used to filter the variables. Receiver operating characteristic (ROC) curves and calibration curves were used to assess the nomogram. All patients were redivided into two groups based on their nomogram total points, and overall survival was compared.

Results

A total of 1194 GNB patients were retrospectively included, with 835 and 359 patients in the development and validation groups, respectively. Five independent prognostic factors, including age, primary tumor site, SEER stage, surgery and chemotherapy, were screened out and included in the nomogram. The consistency index (C-index) of the Cox regression model was 0.862 and 0.827 in the development group and the validation group, respectively. The areas under the receiver operating characteristic (ROC) curve (AUC) showed that the nomogram had good accuracy in predicting 3-, 5- and 10-year overall survival for GNB patients. The calibration curves of the nomogram showed good agreement between the predicted outcomes and the actual observations. The Kaplan-Meier (KM) survival curves revealed that patients with nomogram scores below the median had a better prognosis.

Conclusions

Age, primary tumor site, SEER stage, surgery and chemotherapy may be independent prognostic factors for GNB. We constructed a nomogram based on the SEER database to predict the prognosis of GNB, but further optimization by adding more risk factors is needed for clinical application.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

Disease-Based Prognostication: Neuro-Oncology

Primary malignant and non-malignant brain and other central nervous system (CNS) tumors, while relatively rare, are a disproportionate source of morbidity and mortality. Here we provide a brief overview of approaches to modeling important clinical outcomes, such as overall survival, that are critical for clinical care. Because there are a large number of histologically distinct types of primary malignant and non-malignant brain and other CNS tumors, this chapter will provide an overview of prognostication considerations on the most common primary non-malignant brain tumor, meningioma, and the most common primary malignant brain tumor, glioblastoma. In addition, information on nomograms and how they can be used as individualized prognostication tools by clinicians to counsel patients and their families regarding treatment, follow-up, and prognosis is described. The current state of nomograms for meningiomas and glioblastomas are also provided.

Development and validation of a nomogram to predict cardiac death after radiotherapy for esophageal cancer

Background

Patients frequently die from cardiac causes after radiotherapy for esophageal cancer. Early detection of cardiac death risk in these patients is crucial to improve clinical decision-making and prognosis. Thus, we modeled the risk of cardiac death after irradiation for esophageal cancer.

Methods

A retrospective analysis of 37,599 esophageal cancer cases treated with radiotherapy in the SEER database between 2000 and 2018 was performed. The selected cases were randomly assigned to the model development group (n = 26,320) and model validation group (n = 11,279) at a ratio of 7:3. We identified the risk factors most commonly associated with cardiac death by least absolute shrinkage and selection operator regression analysis (LASSO). The endpoints for model development and validation were 5- and 10-year survival rates. The net clinical benefit of the models was evaluated by decision curve analysis (DCA) and concordance index (C-index). The performance of the models was further assessed by creating a receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC). Kaplan-Meier (K-M) survival analysis was performed on the probability of death. Patients were classified according to death probability thresholds. Five- and ten-year survival rates for the two groups were shown using K-M curves.

Results

The major risk factors for cardiac death were age, surgery, year of diagnosis, sequence of surgery and radiotherapy, chemotherapy and a number of tumors, which were used to create the nomogram. The C-indexes of the nomograms were 0.708 and 0.679 for the development and validation groups, respectively. DCA showed the good net clinical benefit of nomograms in predicting 5- and 10-year risk of cardiac death. The model exhibited moderate predictive power for 5- and 10-year cardiac mortality (AUC: 0.833 and 0.854, respectively), and for the development and validation cohorts (AUC: 0.76 and 0.813, respectively).

Conclusions

Our nomogram may assist clinicians in making clinical decisions about patients undergoing radiotherapy for esophageal cancer based on early detection of cardiac death risk.

Development and validation of a survival nomogram in patients with primary testicular diffuse large B-cell lymphoma

OBJECTIVE

We developed and validated a nomogram for overall survival (OS) and cancer-specific survival (CSS) prediction in patients with primary testicular diffuse large B-cell lymphoma (PT-DLBCL).

METHODS

Patients diagnosed with PT-DLBCL were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were analyzed to establish a nomogram of OS and CSS. Patients were reclassified into high- and low-risk groups; survival was compared using Kaplan-Meier curves and log-rank tests.

RESULTS

We collected 1099 PT-DLBCL cases (2000-2019) from SEER and randomized into training (n = 771) and validation (n = 328) cohorts. In univariate and multivariate Cox regression analyses, five prognostic indicators (age, treatment modality, diagnosis year, Ann Arbor stage, laterality) were used to establish a nomogram of OS and CSS. The nomogram demonstrated excellent discrimination and calibration, with concordance indices in the training and validation cohorts of 0.702 (95% confidence interval [CI], 0.677-0.727) and 0.705 (95% CI 0.67-0.74) for OS and 0.694 (95% CI 0.663-0.725) and 0.680 (95% CI 0.63-0.72) for CSS. The calibration curve and ROC analysis indicated good predictive capability of the nomogram.

CONCLUSIONS

The constructed prognostic model showed good predictive value for PT-DLBCL to assist clinicians in developing individualized treatment strategies.

A Nomogram Model for Stratifying the Risk of Recurrence in Patients with Meningioma After Surgery

BACKGROUND

Here, we aimed to investigate the clinical parameters affecting the recurrence of meningiomas, and to construct a predictive nomogram model, so as to predict the recurrence-free survival (RFS) of meningiomas more accurately.

METHODS

The Clinical, imaging, and pathological data of 155 primary meningioma patients treated surgically from January 2014 to March 2021 were retrospectively analyzed. Independent prognostic factors affecting postoperative recurrence of meningioma were identified by univariate and multivariate Cox regression analyses. A predictive nomogram was established based on independent influence parameters. Subsequently, time-dependent receiver operating characteristic curve, calibration curve, and Kaplan-Meier method were utilized to evaluate the predictive ability of the model.

RESULTS

The multivariate Cox regression analysis showed that tumor size, Ki-67 index, and resection extent had independent prognostic significance, and these parameters were subsequently used to construct a predictive nomogram. Receiver operating characteristic curves indicated that the model was more accurate in predicting RFS than independent factors. Calibration curves suggested that the predicted RFS were similar to the actual observed RFS. In the Kaplan-Meier analysis, the RFS of high-risk cases was obviously shorter than that of low-risk cases.

CONCLUSIONS

The tumor size, Ki-67 index, and extent of resection were independent factors affecting the RFS of meningioma. The predictive nomogram based on these factors can be used as an effective method to stratify the recurrence risk of meningioma and provide a reference for patients to choose personalized treatment.

Analysis of Prognostic Factors and Surgical Management of Elderly Patients with Low-Grade Gliomas

BACKGROUND

The number of elderly patients with low-grade glioma (LGG) is increasing, but their prognostic factors and surgical treatment are still controversial. This paper aims to investigate the prognostic factors of overall survival and cancer-specific survival in elderly patients with LGG and analyze the optimal surgical treatment strategy.

METHODS

Patients in the study were obtained from the Surveillance, Epidemiology, and End Results database and patients were randomized into a training and a test set (7:3). Clinical variables were analyzed by univariate and multivariate Cox regression analysis to screen for significant prognostic factors, and nomograms visualized the prognosis. In addition, survival analysis of elderly patients regarding different surgical management was also analyzed by Kaplan-Meier curves.

RESULTS

Six prognostic factors were screened by univariate and multivariate Cox regression analysis on the training set: tumor site, laterality, histological type, the extent of surgery, radiotherapy, and chemotherapy, and all factors were visualized by nomogram. And we evaluated the accuracy of the nomogram model using consistency index, calibration plots, receiver operator characteristic curves, and decision curve analysis, showing that the nomogram has strong accuracy and applicability. We also found that gross total resection improved overall survival and cancer-specific survival in patients with LGG aged ≥65 years relative to those who did not undergo surgery (P < 0.001).

CONCLUSIONS

Based on the Surveillance, Epidemiology, and End Results database, we created and validated prognostic nomograms for elderly patients with LGG, which can help clinicians to provide personalized treatment services and clinical decisions for their patients. More importantly, we found that older age alone should not preclude aggressive surgery for LGGs.

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.