Nomograms for predicting the overall survival of patients with cerebellar glioma: An analysis of the SEER (Surveillance Epidemiology and End Results) database

Abstract Purpose: At present, our understanding of cerebellar glioma is still not enough. This study collected information on patients in the seer database to determine the predictive factors patients with cerebellar glioma. Patients and Methods: The data of patients with cerebellar glioma diagnosed from 1975 to 2018 were retrieved from the Surveillance Epidemiology and End Results Database. We randomly divide the data into a training group and a validation group, establish a nomogram based on the training group, and use the validation group data to verify the clinical value of the model. Results: A total of 508 patients were included in this study. Multivariate analysis was performed based on the data before randomization, and the results showed that the patient's age, the tumor's WHO grade, histological type, and extension are significantly correlated with the survival rate. The c index of OS nomograms of the training cohort was 0.909 (95% CI,(0.880-0.938）) and 0.932(95% CI, (0.889-0.975)) in validation group. The calibration curve of OS for 3 and 5 years show that there was a good consistency between the actual survival probability and the predicted survival probability.Conclusion: For patients with cerebellar glioma, the age of the patient at the time of diagnosis, WHO grade of the glioma, histological type, and extension are the four most prominent factors that affect the overall survival outcomes. Furthermore, our model may be a useful tool for predicting OS in these patients.

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Nomograms for predicting the overall survival of patients with cerebellar glioma: an analysis of the surveillance epidemiology and end results (SEER) database

Scientific Reports ◽

10.1038/s41598-021-98960-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jie Li ◽

Wobin Huang ◽

Jiajing Chen ◽

Zhuhui Li ◽

Bocong Liu ◽

...

Keyword(s):

Overall Survival ◽

Survival Probability ◽

Training Group ◽

Histological Type ◽

Seer Database ◽

Validation Group ◽

Clinical Value ◽

Who Grade ◽

Group Data ◽

End Results

AbstractAt present, our understanding of cerebellar glioma is still insufficient. This study collected information on patients in the SEER database to identify the predictive factors for patients with cerebellar glioma. Data from patients with cerebellar glioma diagnosed from 1975 to 2018 were retrieved from the Surveillance Epidemiology and End Results Database. We randomly divided the patients into a training group and a validation group, established a nomogram based on the training group, and used the validation group data to verify the clinical value of the model. A total of 508 patients were included in this study. Multivariate analysis was performed based on the data before randomization, and the results showed that the patient's age, WHO grade, histological type, and extent were significantly correlated with the survival rate. The C-index of the OS nomograms of the training cohort was 0.909 (95% CI, (0.880–0.938)) and 0.932 (95% CI, (0.889–0.975)) in the validation group. The calibration curve of OS for 3 and 5 years showed that there was good consistency between the actual survival probability and the predicted survival probability. For patients with cerebellar glioma, the age at diagnosis, WHO grade of the glioma, histological type, and extension are the four factors that most strongly affect the overall survival outcomes. Furthermore, our model may be a useful tool for predicting OS in these patients.

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P10.03 Establishment and validation of clinical prediction model in WHO grade II glioma

Neuro-Oncology ◽

10.1093/neuonc/noab180.096 ◽

2021 ◽

Vol 23 (Supplement_2) ◽

pp. ii28-ii28

Author(s):

X Xue ◽

Q Gao

Keyword(s):

Prediction Model ◽

Training Group ◽

Clinical Prediction ◽

Validation Group ◽

Who Grade ◽

Clinical Prediction Model ◽

Cox Regression Analysis ◽

Who Grade Ii Glioma ◽

Grade Ii ◽

Grade Ii Glioma

Abstract OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.

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Nomograms Predicting Long-Term Overall Survival and Cancer-Specific Survival in Metastatic Hepatocellular Carcinoma Patients

10.21203/rs.3.rs-31444/v1 ◽

2020 ◽

Author(s):

Chi Cui ◽

Yaru Duan ◽

Rui Li ◽

Hua Ye ◽

Peng Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Training Group ◽

Clinicopathological Characteristics ◽

Validation Group ◽

Cancer Specific Survival ◽

Term Survival ◽

Metastatic Hepatocellular Carcinoma

Abstract Background This study aims to evaluate the clinicopathological characteristics of metastatic hepatocellular carcinoma (HCC) patients and develop nomograms to predict their long-term overall survival (OS) and cancer-specific survival (CSS). Methods Information on metastatic HCC from 2010 to 2015 was retrieved from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute. The metastatic HCC patients were divided into a long-term survival (LTS) group and a short-term survival (STS) group with 1 year selected as the cut-off value. Then, we compared the demographic and clinicopathological features between the two groups. Next, all patients were randomly divided into a training group and validation group at a 7:3 ratio. Univariate and multivariate Cox regression analyses were used to identify potential predictors for OS and CSS in the training group, and nomograms of OS and CSS were established. These predictive models were further validated in the validation group. Results A total of 2163 patients were included in the current study according to the inclusion and exclusion criteria. Patients with characteristics including lower T stage and N stage; treatment with surgery, radiation or chemotherapy; no lung metastasis; and AFP negative status showed better survival. The concordance index (C-index) of the OS nomogram was 0.72 based on 9 variables. The C-index of the CSS nomogram was 0.71 based on 8 variables. Conclusions These nomograms may help clinicians make better treatment recommendations for metastatic HCC patients.

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Oligodendroglioma resection: a Surveillance, Epidemiology, and End Results (SEER) analysis

Journal of Neurosurgery ◽

10.3171/2016.11.jns161974 ◽

2018 ◽

Vol 128 (4) ◽

pp. 1076-1083 ◽

Cited By ~ 9

Author(s):

Ali A. Alattar ◽

Michael G. Brandel ◽

Brian R. Hirshman ◽

Xuezhi Dong ◽

Kate T. Carroll ◽

...

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Primary Cns Lymphoma ◽

Extent Of Resection ◽

Subtotal Resection ◽

Cns Lymphoma ◽

Who Grade ◽

Kaplan Meier ◽

Clinical Benefits ◽

End Results

OBJECTIVEThe available evidence suggests that the clinical benefits of extended resection are limited for chemosensitive tumors, such as primary CNS lymphoma. Oligodendroglioma is generally believed to be more sensitive to chemotherapy than astrocytoma of comparable grades. In this study the authors compare the survival benefit of gross-total resection (GTR) in patients with oligodendroglioma relative to patients with astrocytoma.METHODSUsing the Surveillance, Epidemiology, and End Results (SEER) Program (1999–2010) database, the authors identified 2378 patients with WHO Grade II oligodendroglioma (O2 group) and 1028 patients with WHO Grade III oligodendroglioma (O3 group). Resection was defined as GTR, subtotal resection, biopsy only, or no resection. Kaplan-Meier and multivariate Cox regression survival analyses were used to assess survival with respect to extent of resection.RESULTSCox multivariate analysis revealed that the hazard of dying from O2 and O3 was comparable between patients who underwent biopsy only and GTR (O2: hazard ratio [HR] 1.06, 95% confidence interval [CI] 0.73–1.53; O3: HR 1.18, 95% CI 0.80–1.72). A comprehensive search of the published literature identified 8 articles without compelling evidence that GTR is associated with improved overall survival in patients with oligodendroglioma.CONCLUSIONSThis SEER-based analysis and review of the literature suggest that GTR is not associated with improved survival in patients with oligodendroglioma. This finding contrasts with the documented association between GTR and overall survival in anaplastic astrocytoma and glioblastoma. The authors suggest that this difference may reflect the sensitivity of oligodendroglioma to chemotherapy as compared with astrocytomas.

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Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model

Frontiers in Oncology ◽

10.3389/fonc.2021.739933 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jie Kong ◽

Shuchai Zhu ◽

Gaofeng Shi ◽

Zhikun Liu ◽

Jun Zhang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Training Group ◽

Oesophageal Squamous Cell Carcinoma ◽

Lasso Regression ◽

Validation Group ◽

Clinical Value ◽

Free Survival ◽

Enhanced Computed Tomography

Background and PurposeChemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy.Materials and MethodsIn total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model.ResultsOf the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 =0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645–0.787) in the training group and 0.718 (95% CI: 0.612–0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674–0.810) in the training group and 0.715 (95% CI: 0.609–0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy.ConclusionsA radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.

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Nomograms Predicting Long-Term Overall Survival and Cancer-Specific Survival in Metastatic Hepatocellular Carcinoma Patients: A SEER Population-Based Study

10.21203/rs.3.rs-37178/v1 ◽

2020 ◽

Author(s):

Chi Cui ◽

Yaru Duan ◽

Rui Li ◽

Hua Ye ◽

Peng Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Training Group ◽

Population Based ◽

Validation Group ◽

Cancer Specific Survival ◽

Term Survival ◽

Metastatic Hepatocellular Carcinoma

Abstract Background: This study aims to evaluate the clinicopathological characteristics of metastatic hepatocellular carcinoma (HCC) patients and develop nomograms to predict their long-term overall survival (OS) and cancer-specific survival (CSS). Methods: Information on metastatic HCC from 2010 to 2015 was retrieved from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute. The metastatic HCC patients were divided into a long-term survival (LTS) group and a short-term survival (STS) group with 1 year selected as the cut-off value. Then, we compared the demographic and clinicopathological features between the two groups. Next, all patients were randomly divided into a training group and validation group at a 7:3 ratio. Univariate and multivariate Cox regression analyses were used to identify potential predictors for OS and CSS in the training group, and nomograms of OS and CSS were established. These predictive models were further validated in the validation group. Results: A total of 2163 patients were included in the current study according to the inclusion and exclusion criteria. Patients with characteristics including lower T stage and N stage; treatment with surgery, radiation or chemotherapy; no lung metastasis; and AFP negative status showed better survival. The concordance index (C-index) of the OS nomogram was 0.72 based on 9 variables. The C-index of the CSS nomogram was 0.71 based on 8 variables.Conclusions: These nomograms may help clinicians make better treatment recommendations for metastatic HCC patients.

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A noninvasive model to predict liver histology for antiviral therapy decision in chronic hepatitis B with alanine aminotransferase < 2 upper limit of normal

BMC Gastroenterology ◽

10.1186/s12876-020-01576-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shanshan Chen ◽

Haijun Huang ◽

Wei Huang

Keyword(s):

Chronic Hepatitis ◽

Hepatitis B ◽

Liver Biopsy ◽

Antiviral Therapy ◽

Alanine Aminotransferase ◽

Antiviral Treatment ◽

Training Group ◽

Liver Histology ◽

Validation Group ◽

Upper Limit

Abstract Background At present, most assessments of liver fibrosis staging mainly focus on non-invasive diagnostic methods. This study aims to construct a noninvasive model to predict liver histology for antiviral therapy in chronic hepatitis B (CHB) with alanine aminotransferase (ALT) < 2 times upper limit of normal (ULN). Methods We retrospectively analyzed 577 patients with CHB who received liver biopsy and whose ALT was less than 2 ULN. Then they were randomly divided into a training group and a validation group. Through logistic regression analysis, a novel predictive model was constructed in the training group to predict significant changes in liver histology [necro-inflammatory activity grade (G) ≥ 2 or fibrosis stage (S) ≥ 2] and then validated in the validation group. Results If liver biopsy showed moderate or severe inflammation or significant fibrosis, antiviral treatment was recommended. Aspartate aminotransferase (AST), anti-hepatitis B virus core antibody (anti-HBC) and glutamine transpeptidase (GGT) were identified as independent predictors for antiviral therapy, with area under the ROC curve (AUROC) of 0.649, 0.647 and 0.616, respectively. Our novel model index, which combined AST, anti- HBC and GGT with AUROC of 0.700 and 0.742 in training set and validation set. Conclusions This study established a noninvasive model to predict liver histology for antiviral treatment decision in patients with CHB with ALT < 2 ULN, which can reduce the clinical needs of liver biopsy.

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Influence of VEGF-A, VEGFR-1-3, and neuropilin 1-2 on progression-free: and overall survival in WHO grade II and III meningioma patients

Journal of Molecular Histology ◽

10.1007/s10735-020-09940-2 ◽

2021 ◽

Vol 52 (2) ◽

pp. 233-243

Author(s):

Simon Bernatz ◽

Daniel Monden ◽

Florian Gessler ◽

Tijana Radic ◽

Elke Hattingen ◽

...

Keyword(s):

Overall Survival ◽

Tumor Cells ◽

Progression Free Survival ◽

Staining Intensity ◽

Who Grade ◽

Positive Correlation ◽

Protein Levels ◽

Angiogenic Therapy ◽

Neuropilin 1 ◽

Grade Ii

AbstractHigher grade meningiomas tend to recur. We aimed to evaluate protein levels of vascular endothelial growth factor (VEGF)-A with the VEGF-receptors 1-3 and the co-receptors Neuropilin (NRP)-1 and -2 in WHO grade II and III meningiomas to elucidate the rationale for targeted treatments. We investigated 232 specimens of 147 patients suffering from cranial meningioma, including recurrent tumors. Immunohistochemistry for VEGF-A, VEGFR-1-3, and NRP-1/-2 was performed on tissue micro arrays. We applied a semiquantitative score (staining intensity x frequency). VEGF-A, VEGFR-1-3, and NRP-1 were heterogeneously expressed. NRP-2 was mainly absent. We demonstrated a significant increase of VEGF-A levels on tumor cells in WHO grade III meningiomas (p = 0.0098). We found a positive correlation between expression levels of VEGF-A and VEGFR-1 on tumor cells and vessels (p < 0.0001). In addition, there was a positive correlation of VEGF-A and VEGFR-3 expression on tumor vessels (p = 0.0034). VEGFR-2 expression was positively associated with progression-free survival (p = 0.0340). VEGF-A on tumor cells was negatively correlated with overall survival (p = 0.0084). The VEGF-A-driven system of tumor angiogenesis might still present a suitable target for adjuvant therapy in malignant meningioma disease. However, its role in malignant tumor progression may not be as crucial as expected. The value of comprehensive testing of the ligand and all receptors prior to administration of anti-angiogenic therapy needs to be evaluated in clinical trials.

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Nomograms predicting the overall survival and cancer-specific survival of patients with stage IIIC1 cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08209-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yifan Feng ◽

Ye Wang ◽

Yangqin Xie ◽

Shuwei Wu ◽

Yuyang Li ◽

...

Keyword(s):

Cervical Cancer ◽

Overall Survival ◽

Random Sampling ◽

Tumour Size ◽

Training Group ◽

Data Sets ◽

Prognostic Variables ◽

Cancer Specific Survival ◽

External Data ◽

Seer Data

Abstract Background To explore the factors that affect the prognosis of overall survival (OS) and cancer-specific survival (CSS) of patients with stage IIIC1 cervical cancer and establish nomogram models to predict this prognosis. Methods Data from patients in the Surveil-lance, Epidemiology, and End Results (SEER) programme meeting the inclusion criteria were classified into a training group, and validation data were obtained from the First Affiliated Hospital of Anhui Medical University from 2010 to 2019. The incidence, Kaplan-Meier curves, OS and CSS of patients with stage IIIC1 cervical cancer in the training group were evaluated. Nomograms were established according to the results of univariate and multivariate Cox regression models. Harrell’s C-index, calibration plots, receiver operating characteristic (ROC) curves and decision-curve analysis (DCA) were calculated to validate the prediction models. Results The incidence of pelvic lymph node metastasis, a high-risk factor for the prognosis of cervical cancer, decreased slightly over time. Eight independent prognostic variables were identified for OS, including age, race, marriage status, histology, extension range, tumour size, radiotherapy and surgery, but only seven were identified for CSS, with marriage status excluded. Nomograms of OS and CSS were established based on the results. The C-indexes for the nomograms of OS and CSS were 0.687 and 0.692, respectively, using random sampling of SEER data sets and 0.701 and 0.735, respectively, using random sampling of external data sets. The AUCs for the nomogram of OS were 0.708 and 0.705 for the SEER data sets and 0.750 and 0.750 for the external data sets, respectively. In addition, AUCs of 0.707 and 0.709 were obtained for the nomogram of CSS when validated using SEER data sets, and 0.788 and 0.785 when validated using external data sets. Calibration plots for the nomograms were almost identical to the actual observations. The DCA also indicated the value of the two models. Conclusions Eight independent prognostic variables were identified for OS. The same factors predicted CSS, with the exception of the marriage status. Both OS and CSS nomograms had good predictive and clinical application value after validation. Notably, tumour size had the largest contribution to the OS and CSS nomograms.

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Heme Biosynthesis mRNA Expression Signature: Towards a Novel Prognostic Biomarker in Patients with Diffusely Infiltrating Gliomas

Cancers ◽

10.3390/cancers13040662 ◽

2021 ◽

Vol 13 (4) ◽

pp. 662

Author(s):

Mario Mischkulnig ◽

Barbara Kiesel ◽

Daniela Lötsch ◽

Thomas Roetzer ◽

Martin Borkovec ◽

...

Keyword(s):

Overall Survival ◽

Mrna Expression ◽

Postoperative Management ◽

Heme Biosynthesis ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Who Grade ◽

Expression Signature ◽

Cancer Genome Atlas ◽

The Impact

Diffusely infiltrating gliomas are characterized by a variable clinical course, and thus novel prognostic biomarkers are needed. The heme biosynthesis cycle constitutes a fundamental metabolic pathway and might play a crucial role in glioma biology. The aim of this study was thus to investigate the role of the heme biosynthesis mRNA expression signature on prognosis in a large glioma patient cohort. Glioma patients with available sequencing data on heme biosynthesis expression were retrieved from The Cancer Genome Atlas (TCGA). In each patient, the heme biosynthesis mRNA expression signature was calculated and categorized into low, medium, and high expression subgroups. Differences in progression-free and overall survival between these subgroups were investigated including a multivariate analysis correcting for WHO grade, tumor subtype, and patient age and sex. In a total of 693 patients, progression-free and overall survival showed a strictly monotonical decrease with increasing mRNA expression signature subgroups. In detail, median overall survival was 134.2 months in the low, 79.9 months in the intermediate, and 16.5 months in the high mRNA expression signature subgroups, respectively. The impact of mRNA expression signature on progression-free and overall survival was independent of the other analyzed prognostic factors. Our data indicate that the heme biosynthesis mRNA expression signature might serve as an additional novel prognostic marker in patients with diffusely infiltrating gliomas to optimize postoperative management.

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