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

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.

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

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.

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

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.

scholarly journals FAM225B Is a Prognostic lncRNA for Patients with Recurrent Glioblastoma

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.

scholarly journals Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

2020 ◽  
Vol 40 (9) ◽  
Author(s):  
Zihao He ◽  
Tuo Deng ◽  
Xiaolu Duan ◽  
Guohua Zeng
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Family Member ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set

Abstract The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS &gt; 5 years (149 patients) and those with &lt;1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74–0.82), 0.75 (0.70–0.80), and 0.70 (0.60–0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.

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

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.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-15
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

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.

scholarly journals Development and validation of a nomogram to predict overall survival for patients with metastatic renal cell carcinoma

2020 ◽  
Author(s):  
Wenwen Zheng ◽  
Weiwei Zhu ◽  
Shengqiang Yu ◽  
Kangqi Li ◽  
Yuexia Ding ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Staging System ◽  
Survival Prediction ◽  
Seer Database ◽  
Training Set ◽  
Ajcc Staging System ◽  
Validation Set ◽  
Ajcc Staging ◽  
Metastatic Rcc

Abstract Background: The prognosis of metastatic renal cell carcinoma (RCC) patients vary widely because of clinical and pathological heterogeneity. We aimed to develop a novel nomogram to predict overall survival (OS) for this population. Methods: Metastatic RCC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. These patients were randomly assigned to a training set and a validation set at a ratio of 1:1. Significant prognostic factors of survival were identified through Cox regression models and then integrated to form a nomogram to predict 1-, 3- and 5-year OS. The nomogram was subsequently subjected to validations via the training and the validation sets. The performance of this model was evaluated by using Harrell’s concordance index (C-index), calibration curve, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results: Overall, 2315 eligible metastatic RCC patients were enrolled from the SEER database. A nomogram of survival prediction for patients of newly diagnosed with metastatic RCC was established, in which eight clinical factors significantly associated with OS were involved, including Fuhrman grade, lymph node status, sarcomatoid feature, cancer-directed surgery, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. The new model presented better discrimination power than the American Joint Committee on Cancer (AJCC) staging system (7th edition) in the training set (C-indexes, 0.701 vs. 0.612, P <0.001) and the validation set (C-indexes, 0.676 vs. 0.600, P <0.001). The calibration plots of the nomogram exhibited optimal agreement between the predicted values and the observed values. The results of NRI and IDI also indicated the superior predictive capability of the nomogram relative to the AJCC staging system. The DCA plots revealed higher clinical use of our model in survival prediction. Conclusions: We developed and validated an effective nomogram to provide individual OS prediction for metastatic RCC patients, which would be beneficial to clinical trial design, patient counseling, and therapeutic modality selection.

scholarly journals Prognostic Nomograms for Primary High-Grade Glioma Patients in Adult: A Retrospective Study Based on the SEER Database

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yi Yang ◽  
Mingze Yao ◽  
Shengrong Long ◽  
Chengran Xu ◽  
Lun Li ◽  
...  
Keyword(s):  
High Grade Glioma ◽  
Curve Analysis ◽  
Histological Type ◽  
High Grade ◽  
Seer Database ◽  
Operating Characteristics ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Validation Set ◽  
Cox Analysis

Purpose. In our study, we aimed to screen the risk factors that affect overall survival (OS) and cancer-specific survival (CSS) in adult glioma patients and to develop and evaluate nomograms. Methods. Primary high-grade gliomas patients being retrieved from the surveillance, epidemiology and end results (SEER) database, between 2004 and 2015, then they randomly assigned to a training group and a validation group. Univariate and multivariate Cox analysis models were used to choose the variables significantly correlated with the prognosis of high-grade glioma patients. And these variables were used to construct the nomograms. Next, concordance index (C-index), calibration plot and receiver operating characteristics (ROCs) curve were used to evaluate the accuracy of the nomogram model. In addition, the decision curve analysis (DCA) was used to analyze the benefit of nomogram and prognostic indicators commonly used in clinical practice. Results. A total of 6395 confirmed glioma patients were selected from the SEER database, divided into training set (n =3166) and validation set (n =3229). Age at diagnosis, tumor grade, tumor size, histological type, surgical type, radiotherapy and chemotherapy were screened out by Cox analysis model. For OS nomogram, the C-index of the training set was 0.741 (95% CI: 0.751-0.731), and the validation set was 0.738 (95% CI: 0.748-0.728). For CSS nomogram, the C-index of the training set was 0.739 (95% CI: 0.749-0.729), and the validation set was 0.738 (95% CI: 0.748-0.728). The net benefit and net reduction in inverventions of nomograms in the decision curve analysis (DCA) was higher than histological type. Conclusions. We developed nomograms to predict 3- and 5-year OS rates and 3- and 5-year CSS rates in adult high-grade glioma patients. Both the training set and the validation set showed good calibration and validation, indicating the clinical applicability of the nomogram and good predictive results.

scholarly journals 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

2021 ◽  
Author(s):  
Ge Wang ◽  
Xin Ren ◽  
Mengmeng Wang ◽  
Xiaomin Sun ◽  
Yongsheng Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Survival Rate ◽  
Prognostic Model ◽  
Seer Database ◽  
Training Set ◽  
Metaplastic Breast Cancer ◽  
Cox Regression Analysis ◽  
Validation Set

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.

scholarly journals A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study

2022 ◽  
Vol 9 ◽  
Author(s):  
Jie Tang ◽  
JinKui Wang ◽  
Xiudan Pan
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Elderly Patients ◽  
Prediction Model ◽  
Bone Tumors ◽  
Receiver Operating Curve ◽  
Training Set ◽  
Malignant Bone Tumors ◽  
Independent Risk Factors ◽  
Validation Set

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT.Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients.Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

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