15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma

Background. Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients. Methods. The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram. Results. Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally. Conclusion. Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

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15-lncRNAs-based classifier-clinicopathologic nomogram improves the prediction of recurrence in patients with hepatocellular carcinoma

10.21203/rs.2.15723/v1 ◽

2019 ◽

Author(s):

Qiong Zhang ◽

Gang Ning ◽

Hongye Jiang ◽

Yanlin Huang ◽

Jinsong Piao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Clinical Decision Making ◽

Cox Regression ◽

Curve Analysis ◽

Tnm Stage ◽

Training Set ◽

Decision Curve Analysis ◽

Clinicopathologic Factors ◽

Validation Set ◽

Better Than

Abstract Background: Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma(HCC) patients.Methods: The lncRNAs profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from the Cancer Genome Atlas (TCGA). Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier and compared our classifier to existing six-lncRNAs signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with TNM staging system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram.Results: Fifteen relapse-free survival (RFS) -related lncRNAs were identified and the classifer, consisting of the identified15 lncRNAs, could effectively classify patients into high-risk and low-risk subgroup. The prediction accuracy of the 15-lncRNAs-based classifier for predicting 2- year and 5-year RFS were 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, which was better than the existing six-lncRNAs signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. Conclusion: Compared to TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

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An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

Scientific Reports ◽

10.1038/s41598-019-53811-0 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Jie Cui ◽

Qingquan Wen ◽

Xiaojun Tan ◽

Jinsong Piao ◽

Qiong Zhang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Squamous Cell ◽

Calibration Curve ◽

Cox Regression ◽

Curve Analysis ◽

Tnm Stage ◽

Decision Curve Analysis ◽

Clinicopathologic Factors

AbstractLong non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

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Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

Srpski arhiv za celokupno lekarstvo ◽

10.2298/sarh1512681p ◽

2015 ◽

Vol 143 (11-12) ◽

pp. 681-687 ◽

Cited By ~ 1

Author(s):

Tomislav Pejovic ◽

Miroslav Stojadinovic

Keyword(s):

Bile Duct ◽

Clinical Utility ◽

Clinical Decision Making ◽

Prediction Models ◽

Open Cholecystectomy ◽

Univariate Analysis ◽

Curve Analysis ◽

Decision Curve Analysis ◽

Scoring Model ◽

Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

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Prediction of vaginal birth after cesarean delivery in southeast China: A retrospective cohort study

10.21203/rs.3.rs-30089/v1 ◽

2020 ◽

Author(s):

Hua-Le Zhang ◽

Liang-Hui Zheng ◽

Li-Chun Cheng ◽

Zhao-Dong Liu ◽

Lu Yu ◽

...

Keyword(s):

Cesarean Section ◽

Pregnant Women ◽

Success Rate ◽

Gestational Age ◽

Curve Analysis ◽

Inclusion Criteria ◽

Vaginal Birth After Cesarean ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set

Abstract Objective To develop and validate a nomogram to better predict the vaginal birth after cesarean (VBAC) on the premise of clinical guide application. Methods We retrospectively identified hospitalised pregnant women who trial of labor after cesarean (TOLAC) between October 2015 and October 2017 using data from the Fujian Provincial Maternity and Children's Hospital. The inclusion criteria were as follows: Singleton pregnant women whose gestational age was above 37 weeks and underwent a primary cesarean section. Sociodemographic data and Clinical Characteristics were extracted. The samples were randomly divided into a training set and a validation set. Least absolute shrinkage and selection operator (LASSO) regression were used to select variables and construct of VBAC success rate in training set. The validation of the nomogram was performed using the concordance index (C-index), decision curve analysis (DCA), and calibration curves in the validation set. For comparison with published VBAC prediction models, the Grobman’s model was used. Results Among the 708 pregnant women included according to inclusion criteria, 586 (82.77%) patients were successfully for VBAC. In multivariate logistic regression models, Maternal height (OR, 1.11; 95% CI, 1.04 to 1.19), maternal BMI at delivery (OR, 0.89; 95% CI, 0.79 to 1.00), fundal height (OR, 0.71; 95% CI, 0.58 to 0.88), cervix Bishop score (OR, 3.27; 95% CI, 2.49 to 4.45), maternal age at delivery (OR, 0.90; 95% CI, 0.82 to 0.98), gestational age (OR, 0.33; 95% CI, 0.17 to 0.62) and history of vaginal delivery (OR, 2.92; 95% CI, 1.42 to 6.48) were independently associated with successful VBAC. The predictive model was constructed showed better discrimination in the validation series than Grobman’s model (c-index 0.906 VS 0.694, respectively). Decision curve analysis revealed that the new model resulted in a better clinical net benefit than the Grobman’s model. Conclusions The promotion of VBAC is helpful to reduce the cesarean section rate in China. On the basis of following the clinical practice guidelines, the TOLAC prediction model helps to improve the success rate of VBAC and has a potential contribution to the reduction of secondary cesarean section.

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Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

10.21203/rs.3.rs-123528/v1 ◽

2020 ◽

Author(s):

Ruyi Zhang ◽

Mei Xu ◽

Xiangxiang Liu ◽

Miao Wang ◽

Qiang Jia ◽

...

Keyword(s):

Thyroid Carcinoma ◽

External Validation ◽

Cancer Staging ◽

Curve Analysis ◽

Calibration Plot ◽

Net Benefit ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set ◽

Good Calibration

Abstract Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

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MRI-Based Radiomics Nomogram To Predict Intraoperative Hemorrhage of Placenta Previa

10.21203/rs.3.rs-457028/v2 ◽

2021 ◽

Author(s):

Ye Song ◽

Liping Zhu ◽

Dali Chen ◽

Yongmei Li ◽

Qi Xi ◽

...

Keyword(s):

Predictive Accuracy ◽

Placenta Previa ◽

Preoperative Evaluation ◽

Curve Analysis ◽

Training Set ◽

Decision Curve Analysis ◽

Preoperative Prediction ◽

Mri Features ◽

Wide Range ◽

Validation Set

Abstract Background: Placenta previa is associated with higher percentage of intraoperative and postpartum hemorrhage, increased obstetric hysterectomy, significant maternal morbidity and mortality. We aimed to develop and validate a magnetic resonance imaging (MRI)-based nomogram to preoperative prediction of intraoperative hemorrhage (IPH) for placenta previa, which might contribute to adequate assessment and preoperative preparation for the obstetricians.Methods: Between May 2015 and December 2019, a total of 125 placenta previa pregnant women were divided into a training set (n = 80) and a validation set (n = 45). Radiomics features were extracted from MRI images of each patient. A MRI-based model comprising seven features was built for the classification of patients into IPH and non-IPH groups in a training set and validation set. Multivariate nomograms based on logistic regression analyses were built according to radiomics features. Receiver operating characteristic (ROC) curve was used to assess the model. Predictive accuracy of nomogram were assessed by calibration plots and decision curve analysis. Results: In multivariate analysis, placenta position, placenta thickness, cervical blood sinus and placental signals in the cervix were signifcantly independent predictors for IPH (all p < 0.05). The MRI-based nomogram showed favorable discrimination between IPH and non-IPH groups. The calibration curve showed good agreement between the estimated and the actual probability of IPH. Decision curve analysis also showed a high clinical benefit across a wide range of probability thresholds. The AUC was 0.918 ( 95% CI, 0.857-0.979 ) in the training set and 0.866( 95% CI, 0.748-0.985 ) in the validation set by the combination of four MRI features.Conclusions: The MRI-based nomograms might be a useful tool for the preoperative prediction of IPH outcomes for placenta previa. Our study enables obstetricians to perform adequate preoperative evaluation to minimize blood loss and reduce the rate of caesarean hysterectomy.

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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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Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

Frontiers in Oncology ◽

10.3389/fonc.2021.701598 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yaoyao Zhuo ◽

Yi Zhan ◽

Zhiyong Zhang ◽

Fei Shan ◽

Jie Shen ◽

...

Keyword(s):

Clinical Features ◽

Ct Images ◽

Curve Analysis ◽

Good Prediction ◽

Training Set ◽

Minimum Diameter ◽

Decision Curve Analysis ◽

Thin Slice ◽

Solid Nodule ◽

Validation Set

AimTo investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN).Materials and MethodsA total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model.ResultsA total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99–1.00] and validation set (AUC, 0.99; 95% CI, 0.98–1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings.ConclusionThe radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.

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A genomic-clinicopathologic nomogram predict survival for patients with laryngeal squamous cell carcinoma

10.21203/rs.2.12975/v1 ◽

2019 ◽

Author(s):

Jie Cui ◽

Qingquan Wen ◽

Xiaojun Tan ◽

Jinsong Piao ◽

Qiong Zhang ◽

...

Keyword(s):

Overall Survival ◽

Squamous Cell Carcinoma ◽

Calibration Curve ◽

Survival Probability ◽

Area Under Curve ◽

Laryngeal Squamous Cell Carcinoma ◽

Cox Regression ◽

Curve Analysis ◽

Tnm Stage ◽

Decision Curve Analysis

Abstract Long non-coding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. We aimed to establish a lncRNAs signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC). LSCC RNA sequencing (RNA-seq) data set and the matched clinicopathologic information were downloaded from the Cancer Genome Atlas (TCGA). Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed a thirteen lncRNAs signature related to prognosis. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to assess clinical value of our nomogram. Consequently, thirteen overall survival (OS) -related lncRNAs were identified, and the signature consisting of the selected thirteen lncRNAs could effectively divide patients into high-risk and low-risk subgroup, with the area under curve (AUC) of 0.89 (3-year OS) and AUC of 0.885(5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status and lncRNAs signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram were in good coincide with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of nomogram in predicting overall survival (OS) was 0.938, which were significantly higher than traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage. In summary, an inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

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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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