Construction and Validation of a Prognostic Model for the Assessment of Postoperative Overall Survival of Patients with Metaplastic Breast Cancer: Based on a Retrospective Large Data Analysis and Chinese Multicenter Study

Abstract Purpose: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods: Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans. Abstract Background: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods： Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans.

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Development and validation of a prognostic model for overall survival in chemotherapy-naive men with metastatic castration-resistant prostate cancer (mCRPC) from the phase 3 prevail clinical trial.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.5022 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 5022-5022

Author(s):

Andrew J. Armstrong ◽

Ping Lin ◽

Celestia S. Higano ◽

Cora N. Sternberg ◽

Guru Sonpavde ◽

...

Keyword(s):

Clinical Trial ◽

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Risk Groups ◽

Multivariable Model ◽

Training Set ◽

Phase 3 ◽

Testing Set

5022 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naive men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naive men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times. Clinical trial information: NCT01212991.

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Risk Stratification Model for Predicting the Overall Survival of Elderly Triple-Negative Breast Cancer Patients: A Population-Based Study

Frontiers in Medicine ◽

10.3389/fmed.2021.705515 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiaozhu Liu ◽

Song Yue ◽

Haodong Huang ◽

Minjie Duan ◽

Binyi Zhao ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Risk Stratification ◽

Elderly Patients ◽

Triple Negative ◽

Training Set ◽

Medium Risk ◽

Validation Set ◽

Stratification System ◽

Risk Stratification System

Background: The objective of this study was to evaluate the prognostic value of clinical characteristics in elderly patients with triple-negative breast cancer (TNBC).Methods: The cohort was selected from the Surveillance, Epidemiology, and End Results (SEER) program dating from 2010 to 2015. Univariate and multivariate analyses were performed using a Cox proportional risk regression model, and a nomogram was constructed to predict the 1-, 3-, and 5-year prognoses of elderly patients with TNBC. A concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to verify the nomogram.Results: The results of the study identified a total of 5,677 patients who were randomly divided 6:4 into a training set (n = 3,422) and a validation set (n = 2,255). The multivariate analysis showed that age, race, grade, TN stage, chemotherapy status, radiotherapy status, and tumor size at diagnosis were independent factors affecting the prognosis of elderly patients with TNBC. Together, the 1 -, 3 -, and 5-year nomograms were made up of 8 variables. For the verification of these results, the C-index of the training set and validation set were 0.757 (95% CI 0.743–0.772) and 0.750 (95% CI 0.742–0.768), respectively. The calibration curve also showed that the actual observation of overall survival (OS) was in good agreement with the prediction of the nomograms. Additionally, the DCA showed that the nomogram had good clinical application value. According to the score of each patient, the risk stratification system of elderly patients with TNBC was further established by perfectly dividing these patients into three groups, namely, low risk, medium risk, and high risk, in all queues. In addition, the results showed that radiotherapy could improve prognosis in the low-risk group (P = 0.00056), but had no significant effect in the medium-risk (P < 0.4) and high-risk groups (P < 0.71). An online web app was built based on the proposed nomogram for convenient clinical use.Conclusion: This study was the first to construct a nomogram and risk stratification system for elderly patients with TNBC. The well-established nomogram and the important findings from our study could guide follow-up management strategies for elderly patients with TNBC and help clinicians improve individual treatment.

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A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.722992 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jing Li ◽

Jiajia Du ◽

Yanhong Wang ◽

Hongyan Jia

Keyword(s):

Breast Cancer ◽

Invasive Ductal Carcinoma ◽

Prognostic Model ◽

Ductal Carcinoma ◽

Risk Group ◽

Metastatic Breast ◽

Related Gene ◽

Training Set ◽

Gene Set ◽

Validation Set

Background: Invasive ductal carcinoma (IDC) is the most common type of metastatic breast cancer. Due to the lack of valuable molecular biomarkers, the diagnosis and prognosis of IDC remain a challenge. A large number of studies have confirmed that coagulation is positively correlated with angiogenesis-related factors in metastatic breast cancer. Therefore, the purpose of this study was to construct a COAGULATION-related genes signature for IDC using the bioinformatics approaches.Methods: The 50 hallmark gene sets were obtained from the molecular signature database (MsigDB) to conduct Gene Set Variation Analysis (GSVA). Gene Set Enrichment Analysis (GSEA) was applied to analyze the enrichment of HALLMARK_COAGULATION. The COAGULATION-related genes were extracted from the gene set. Then, Limma Package was used to identify the differentially expressed COAGULATION-related genes (DECGs) between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) samples in GSE26340 data set. A total of 740 IDC samples from The Cancer Genome Atlas (TCGA) database were divided into a training set and a validation set (7:3). The univariate and multivariate Cox regression analyses were performed to construct a risk signature, which divided the IDC samples into the high- and low-risk groups. The overall survival (OS) curve and receiver operating characteristic (ROC) curve were drawn in both training set and validation set. Finally, a nomogram was constructed to predict the 1-, 2-, 3-, 4-, and 5-year survival rates of IDC patients. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic genes.Results: The “HALLMARK_COAGULATION” was significantly activated in IDC. There was a significant difference in the clinicopathological parameters between the DCIS and IDC patients. Twenty-four DECGs were identified, of which five genes (SERPINA1, CAPN2, HMGCS2, MMP7, and PLAT) were screened to construct the prognostic model. The high-risk group showed a significantly lower survival rate than the low-risk group both in the training set and validation set (p=3.5943e-06 and p=0.014243). The risk score was demonstrated to be an independent predictor of IDC prognosis. A nomogram including risk score, pathological_stage, and pathological_N provided a quantitative method to predict the survival probability of 1-, 2-, 3-, 4-, and 5-year in IDC patients. The results of decision curve analysis (DCA) further demonstrated that the nomogram had a high potential for clinical utility.Conclusion: This study established a COAGULATION-related gene signature and showed its prognostic value in IDC through a comprehensive bioinformatics analysis, which may provide a potential new prognostic mean for patients with IDC.

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Construction of a nomogram to predict overall survival for patients with lung large cell neuroendocrine carcinoma: analysis of the SEER database

10.21203/rs.3.rs-16158/v1 ◽

2020 ◽

Author(s):

Dong Han ◽

Fei Gao ◽

Nan Li ◽

Hao Wang ◽

Qi Fu

Keyword(s):

Overall Survival ◽

Survival Rate ◽

Neuroendocrine Carcinoma ◽

Cox Regression ◽

Risk Group ◽

Multivariable Analysis ◽

Large Cell ◽

Large Cell Neuroendocrine Carcinoma ◽

Seer Database ◽

Cox Regression Analysis

Abstract Background Lung large cell neuroendocrine carcinoma (L-LCNEC) has a poor prognosis with lower survival rate than other NSCLC patients. The estimation of an individual survival rate is puzzling. The main purpose of this study was to establish a more accurate model to predict the prognosis of L-LCNEC.Methods Patients aged 18 years or older with L-LCNEC were identified from the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2015. Cox regression analysis was used to identify factors associated with survival time. The results were used to construct a nomogram to predict 1-year, and 3-year survival probability in L-LCNEC patients. Overall survival (OS) were compared between low risk group and high risk group by the Kaplan–Meier analysis.Results A total of 3216 patients were included in the study. We randomly divided all included patients into 7:3 training and validating groups. In multivariable analysis of training cohort, age at diagnosis, sex, stage of tumor, surgical treatment, radiotherapy and chemotherapy were independent prognostic factors for OS. All these factors were incorporated to construct a nomogram, which was tested in the validating cohort.Conclusions we constructed a visual nomogram prognosis model, which had the potential to predict the 1-year and 3-year survival rate of L-LCNEC patients, and could be used as an assistant prediction tool in clinical practice.

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Development and Validation of Nomograms to Predict Prognosis of Oral and Oropharyngeal Mucoepidermoid Carcinoma: A SEER-Based Study

10.21203/rs.3.rs-125136/v1 ◽

2020 ◽

Author(s):

muyuan liu ◽

Litian Tong ◽

Manbin Xu ◽

Xiang Xu ◽

Bin Liang ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Mucoepidermoid Carcinoma ◽

Cox Regression ◽

Seer Database ◽

Disease Specific Survival ◽

Cox Regression Analysis ◽

T Stage ◽

N Stage ◽

Disease Specific

Abstract Background: Due to the low incidence of mucoepidermoid carcinoma, there lacks sufficient studies for determining optimal treatment and predicting prognosis. The purpose of this study was to develop prognostic nomograms, to predict overall survival and disease-specific survival (DSS) of oral and oropharyngeal mucoepidermoid carcinoma patients, using the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Methods: Clinicopathological and follow-up data of patients diagnosed with oral and oropharyngeal mucoepidermoid carcinoma between 2004 and 2017 were collected from the SEER database. The Kaplan-Meier method with the log-rank test was employed to identify single prognostic factors. Multivariate Cox regression was utilized to identify independent prognostic factors. C-index, area under the ROC curve (AUC) and calibration curves were used to assess performance of the prognostic nomograms. Results: A total of 1230 patients with oral and oropharyngeal mucoepidermoid carcinoma were enrolled in the present study. After multivariate Cox regression analysis, age, sex, tumor subsite, T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for overall survival. T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for disease-specific survival. Nomograms were constructed to predict the overall survival and disease-specific survival based on the independent prognostic factors. The fitted nomograms possessed excellent prediction accuracy, with a C-index of 0.899 for OS prediction and 0.893 for DSS prediction. Internal validation by computing the bootstrap calibration plots, using the validation set, indicated excellent performance by the nomograms. Conclusion: The prognostic nomograms developed, based on individual clinicopathological characteristics, in the present study, accurately predicted the overall survival and disease-specific survival of patients with oral and oropharyngeal mucoepidermoid carcinoma.

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Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

Journal of Translational Medicine ◽

10.1186/s12967-020-02522-6 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Wei Ma ◽

Fangkun Zhao ◽

Xinmiao Yu ◽

Shu Guan ◽

Huandan Suo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Curve Analysis ◽

Time Dependent ◽

Training Set ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. Methods We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. Results A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p = 1.215e − 06 in the training set; p = 0.0069 in the validation set; p = 1.233e − 07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR = 1.432; 95% CI 1.204–1.702, p < 0.001), validation set (HR = 1.162; 95% CI 1.004–1.345, p = 0.044), and whole set (HR = 1.240; 95% CI 1.128–1.362, p < 0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

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Development and validation of a prognostic model for overall survival in chemotherapy-naïve men with metastatic castration-resistant prostate cancer (mCRPC) from the phase III PREVAIL clinical trial.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.6_suppl.138 ◽

2017 ◽

Vol 35 (6_suppl) ◽

pp. 138-138

Author(s):

Andrew J. Armstrong ◽

Ping Lin ◽

Celestia S. Higano ◽

Cora N. Sternberg ◽

Guru Sonpavde ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Specific Antigen ◽

Risk Groups ◽

Phase Iii ◽

Multivariable Model ◽

Training Set ◽

Testing Set

138 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naïve men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naïve men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times.

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FAM225B Is a Prognostic lncRNA for Patients with Recurrent Glioblastoma

Disease Markers ◽

10.1155/2020/8888085 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Junsheng Li ◽

Qian Zhang ◽

Peicong Ge ◽

Chaofan Zeng ◽

Fa Lin ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Value ◽

Cox Regression ◽

Recurrent Glioblastoma ◽

Regulation Of Transcription ◽

Training Set ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Validation Set ◽

Pathway Analyses

Objective. The overall survival of patients with recurrent glioblastoma (rGBM) is quite different, so clinical outcome prediction is necessary to guide personalized clinical treatment for patients with rGBM. The expression level of lncRNA FAM225B was analyzed to determine its prognostic value in rGBMs. Methods. We collected 109 samples of Chinese Glioma Genome Atlas (CGGA) RNA sequencing dataset and divided into training set and validation set. Then, we analyzed the expression of FAM225B, clinical characteristics, and overall survival (OS) information. Kaplan-Meier survival analysis was used to estimate the OS distributions. The prognostic value of FAM225B in rGBMs was tested by univariate and multivariate Cox regression analyses. Moreover, we analyzed the biological processes and signaling pathways of FAM225B. Results. We found that FAM225B was upregulated in rGBMs ( P = 0.0009 ). The expression of FAM225B increased with the grades of gliomas ( P < 0.0001 ). The OS of rGBMs in the low-expression group was significantly longer than that in the high-expression group ( P = 0.0041 ). Similar result was found in the training set ( P = 0.0340 ) and verified in the validation set ( P = 0.0292 ). In multivariate Cox regression analysis, FAM225B was identified to be an independent prognostic factor for rGBMs ( P = 0.003 ). Biological process and KEGG pathway analyses implied FAM225B mainly played a functional role on transcription, regulation of transcription, cell migration, focal adhesion, etc. Conclusions. FAM225B is expected to be as a new prognostic biomarker for the identification of rGBM patients with poor outcome. And our study provided a potential therapeutic target for rGBMs.

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Novel Nomograms to Predict of Overall Survival and Cancer-specific Survival of Patients of Metaplastic Breast Cancer

10.21203/rs.3.rs-96114/v1 ◽

2020 ◽

Author(s):

Yongfeng Li ◽

Daobao Chen ◽

Haojun Xuan ◽

Mihnea P. Dragomir ◽

George A. Calin ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Prognostic Factors ◽

Cox Model ◽

Model Performance ◽

Metaplastic Breast Cancer ◽

Cancer Specific Survival ◽

Rare Type ◽

Prognostic Prediction ◽

Cox Analysis

Abstract Background Metaplastic breast cancer (MBC) is a rare type of breast cancer with an increasing incidence, we aim to develop clinical nomograms to predict the overall survival and cancer-specific survival for patients with MBC.MethodsPatients data were collected from the SEER database between 1973 and 2015. All included patients were randomly assigned into the training and validation sets. Univariate and multivariate Cox analysis were performed to identify independent prognostic factors of MBC. These essential prognostic variables were combined to construct nomogram models to predict overall survival (OS) and cancer-specific survival (CSS) in patients with MBC. Model performance was evaluated by concordance index (C-index) and calibration plots.ResultsA total of 1835 patients were collected and divided into the training (1223) and validation (612) groups. The multivariate Cox model identified age, TNM stage, T stage, and N stage, chemotherapy and radiotherapy as independent covariates associated with OS, while these variables except for age and chemotherapy were independent prognostic factors of CSS. The nomogram constructed based on these covariates demonstrated excellent accuracy in estimating 3-, and 5-year OS and CSS, with a C-index of 0.759 (95% CI, 0.746-0.772) for OS and 0.766 (95% CI, 0.751-0.781) for CSS in the training cohort. In the validation cohort, the nomogram-predicted C-index was 0.754 for OS (95%CI, 0.734-0.774) and 0.752 (95%CI, 0.728-0.776) for CSS. All calibration curves exhibited good consistency between predicted and actual survival.ConclusionsThese nomogram models established in this study can help to enhance the accuracy of prognostic prediction, which may thereby improve individualized assessment of survival risks and facilitate to provide constructive therapeutic suggestions.

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Prognostic Nomogram and a Risk Classification System for Predicting Overall Survival of Elderly Patients with Fibrosarcoma: A Population-Based Study

Journal of Oncology ◽

10.1155/2021/9984217 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Fengkai Yang ◽

Hangkai Xie ◽

Yucheng Wang

Keyword(s):

Overall Survival ◽

Elderly Patients ◽

Classification System ◽

Cox Regression ◽

Predictive Accuracy ◽

Risk Classification ◽

Seer Database ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set

Background. The objective of this study was to develop a nomogram model and risk classification system to predict overall survival in elderly patients with fibrosarcoma. Methods. The study retrospectively collected data from the Surveillance, Epidemiology, and End Results (SEER) database relating to elderly patients diagnosed with fibrosarcoma between 1975 and 2015. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses on the training set to construct a nomogram model for predicting the overall survival of patients at 3, 5, and 10 years. The receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the discrimination and predictive accuracy of the model. Decision curve analysis was used for assessing the clinical utility of the model. Result. A total of 357 elderly fibrosarcoma patients from the SEER database were included in our analysis, randomly classified into a training set (252) and a validation set (105). The multivariate Cox regression analysis of the training set demonstrated that age, surgery, grade, chemotherapy, and tumor stage were independent prognostic factors. The ROC showed good model discrimination, with AUC values of 0.837, 0.808, and 0.806 for 3, 5, and 10 years in the training set and 0.769, 0.779, and 0.770 for 3, 5, and 10 years in the validation set, respectively. The calibration curves and decision curve analysis showed that the model has high predictive accuracy and a high clinical application. In addition, a risk classification system was constructed to differentiate patients into three different mortality risk groups accurately. Conclusion. The nomogram model and risk classification system constructed by us help optimize patients’ treatment decisions to improve prognosis.

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