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.

A Web-based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: a Population-based Study

2021 ◽  
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

Abstract 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 nomagram 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. Decision curve analysis(DCA) was used to evaluate the clinical potential application value of nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. Kaplan-Meier (K-M) curve was used to test the difference in survival between the two groups of patients.Results: A total of 1641 patients were included, and they were randomly assigned to the training set (N=1156) 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 set and the validation set also showed similar results. The calibration curves of the training set and the validation set both showed that the observed value and the predicted value 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.

scholarly journals A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Hypopharyngeal Squamous Cell Carcinomas: A Population-Based Study

2022 ◽  
Vol 9 ◽  
Author(s):  
JinKui Wang ◽  
XiaoZhu Liu ◽  
Jie Tang ◽  
Qingquan Zhang ◽  
Yuanyang Zhao
Keyword(s):  
Risk Factors ◽  
Elderly Patients ◽  
Calibration Curve ◽  
Tumor Size ◽  
Squamous Cell Carcinomas ◽  
Training Set ◽  
Cancer Specific Survival ◽  
N Stage ◽  
Independent Risk Factors ◽  
Validation Set

Background: Hypopharyngeal squamous cell carcinomas (HPSCC) is one of the causes of death in elderly patients, an accurate prediction of survival can effectively improve the prognosis of patients. However, there is no accurate assessment of the survival prognosis of elderly patients with HPSCC. The purpose of this study is to establish a nomogram to predict the cancer-specific survival (CSS) of elderly patients with HPSCC.Methods: The clinicopathological data of all patients from 2004 to 2018 were downloaded from the SEER database. These patients were randomly divided into a training set (70%) and a validation set (30%). The univariate and multivariate Cox regression analysis confirmed independent risk factors for the prognosis of elderly patients with HPSCC. A new nomogram was constructed to predict 1-, 3-, and 5-year CSS in elderly patients with HPSCC. Then used the consistency index (C-index), the calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical value of the model.Results: A total of 3,172 patients were included in the study, and they were randomly divided into a training set (N = 2,219) and a validation set (N = 953). Univariate and multivariate analysis suggested that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage were independent risk factors for patient prognosis. These nine variables are included in the nomogram to predict the CSS of patients. The C-index for the training set and validation was 0.713 (95% CI, 0.697–0.729) and 0.703 (95% CI, 0.678–0.729), respectively. The AUC results of the training and validation set indicate that this nomogram has good accuracy. The calibration curve indicates that the observed and predicted values are highly consistent. DCA indicated that the nomogram has a better clinical application value than the traditional TNM staging system.Conclusion: This study identified risk factors for survival in elderly patients with HPSCC. We found that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage are independent prognostic factors. A new nomogram for predicting the CSS of elderly HPSCC patients was established. This model has good clinical application value and can help patients and doctors make clinical decisions.

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 A Risk Prediction Model for Severe Chemotherapy-Induced Anemia in Breast Cancer Patients

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2399-2399 ◽  
Author(s):  
Chun Chao ◽  
Lanfang Xu ◽  
Leila Family ◽  
Hairong Xu
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Prediction Model ◽  
Risk Prediction ◽  
Research Funding ◽  
Risk Prediction Model ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Final Model ◽  
Validation Set

Abstract Introduction: Chemotherapy induced anemia (CIA) is associated with an array of symptoms that can negatively impact patients' quality of life. The incidence and severity of CIA vary significantly depending on the cancer type and chemotherapy regimen administered. Several patient characteristics, such as age, gender, renal function and pre-treatment hemoglobin (Hb) and albumin level have also been reported to be associated with the risk of CIA. However, a comprehensive risk prediction model for CIA is lacking. Here we sought to develop a risk prediction model for severe CIA (Hb<8 g/dl) in breast cancer patients that accounts for detailed chemotherapy regimens and novel risk factors for anemia. Methods: Women diagnosed with incident breast cancer at age 18 and older between 2000-2012 at Kaiser Permanente Southern California (KPSC)and initiated myelosuppressivechemotherapy before June 30, 2013 were included. Women who did not have any hemoglobin measurement prior or during the course of chemotherapy were excluded. Those who had the following conditions prior to chemotherapy were also excluded: less than 12 months KPSC membership, anemia, transfusion, radiation therapy or bone marrow transplant. Potential predictors considered included established CIA risk factors, such as patient demographic characteristics, cancer stage at diagnosis, chemotherapy regimens, and laboratory measurements (Table 1). In addition, several novel risk factors were also evaluated for their ability to predict severe CIA; these included recent cancer surgery and radiation therapy, chronic comorbidities (Table 1) and mediation use (Table 1).All data were collected from KPSC's electronic health records. The cohort was randomly split into a training set (50%) and a validation set (50%). Logistic regression was used to develop the risk prediction model for severe CIA. Predictors that showed a crude association with severe CIA with an odds ratio > 1.5 or <0.67 (i.e., 1/1.5) or a p-value <0.10 in the training set were included for predictive model selection. A stepwise model selection method was used with a p-value cut-off at 0.05. The model performance of the selected final model was evaluated in the validation set usingHosmer-Lemeshow goodness of fit test and the area underthe receiver operating characteristiccurve (AUC). Results: A total of 11,291 breast cancer patients were included in the study. The mean age at diagnosis was 55 years. The majority of the patients were of non-Hispanic white race/ethnicity (57%). Of these, 3.0% developed severe CIA during chemotherapy. The following factors were positively associated with risk of developing severe anemia in the crude analyses and were thus included for model selection: age >65, advanced stages, length of KPSC membership, time between cancer diagnosis to chemotherapy, prior radiation therapy, vascular disease, renal disease, hypertension, osteoarthritis, use of steroids, use of diuretics, use of calcium channel blockers, use of statins, chemotherapy regimens, prior surgery, anti-coagulant use, calendar periods, and baseline ALP, HCT, HGB, lymphocyte count, MCH, MCV, ANC, platelet, RBC, RDW, WBC and GFR (calculated from creatinine). The final model included age, stage, chemotherapy regimen, corticosteroid use, and baseline Hb, MCV and GFR. The odds ratio and 95% confidence interval estimates of variables in the final model in the training set and the validation set are both shown in Table 2. This prediction model achieved an AUC of 0.76 in the validation set, and passed the goodness-of-fit test (test statistics was 0.17). Conclusion: The risk prediction model incorporating traditional and novel CIA risk factors appeared to perform well and may assist clinicians to increase surveillance for patients at high risk of severe CIA during chemotherapy. Disclosures Chao: Amgen Inc.: Research Funding. Xu:Amgen Inc.: Research Funding. Family:Amgen Inc.: Research Funding. Xu:Amgen Inc.: Research Funding.

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.

Nomograms to predict lung metastasis probability and lung metastasis subgroup survival in malignant bone tumors

2021 ◽  
Vol 17 (6) ◽  
pp. 649-661
Author(s):  
Jie Wang ◽  
Yonggang Fan ◽  
Lei Xia
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Prognostic Factors ◽  
High Risk ◽  
Lung Metastasis ◽  
Bone Tumors ◽  
Malignant Bone Tumors ◽  
Tumor Extension ◽  
N Stage ◽  
Selection Operator

The aim of this study was to construct and validate nomograms for predicting lung metastasis and lung metastasis subgroup overall survival in malignant primary osseous neoplasms. Least absolute shrinkage and selection operator, logistic and Cox analyses were used to identify risk factors for lung metastasis in malignant primary osseous neoplasms and prognostic factors for overall survival in the lung metastasis subgroup. Further, nomograms were established and validated. A total of 3184 patients were collected. Variables including age, histology type, American Joint Committee on Cancer T and N stage, other site metastasis, tumor extension and surgery were extracted for the nomograms. The authors found that nomograms could provide an effective approach for clinicians to identify patients with a high risk of lung metastasis in malignant primary osseous neoplasms and perform a personalized overall survival evaluation for the lung metastasis subgroup.

scholarly journals Development and validation of a nomogram for predicting carbapenem-resistant organism infection and prognostic risk factors analysis in elderly hospitalized patients

2020 ◽  
Author(s):  
Yao Wang ◽  
Huiling Li ◽  
Ying Chen ◽  
Yanjun Fu ◽  
Jianfeng Xi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Nosocomial Infection ◽  
Elderly Patients ◽  
Resistant Organism ◽  
Urinary Catheters ◽  
Training Set ◽  
Good Discrimination ◽  
Carbapenem Resistant ◽  
Influence Curve ◽  
Validation Set

Abstract Background: With the extensive use of carbapenem-resistant organism (CRO), CRO infection is constantly detected clinically which has limited available drugs. There have many mechanisms of CRO resistance and rapid horizontal transmission, while the elderly with low resistance is more likely to acquire nosocomial infection. We aimed to screen elderly patients for nosocomial CRO infection and potential risk factors for prognosis. Methods: A total of 177 patients with CRO and carbapenem-sensitive organism (CSO) infection were included in this study. A least absolute shrinkage and selection operator (LASSO) analysis was used to select variables. The nomogram was constructed with multivariable logistic regression. The performance of the model was assessed by the receiver operating characteristic(ROC) curve, calibration, decision curve , and clinical influence curve. Using X-tile to stratify the prognosis risk, Kaplan-Meier curve for risk assessment. Results: Respiratory diseases, mechanical ventilation, indwelling urinary catheters , and APACHE II over 20 were selected as predictors of the CRO infection model. The model showed good discrimination and consistency in the training set and the validation set. The area under the ROC was 0.840 (95% CI: 0.773-0.900)in the training set and 0.822 (95% CI: 0.678-0.936) in the validation set. Decision analysis and influence curve showed that the model was clinically useful. Hepatobiliary diseases, indwelling urinary catheters, and hospital stays longer than 20 days were used as prognostic predictors. After analysis, the prognostic model demonstrated good discrimination of 0.817 (95% CI: 0.729-0.893) and consistency. Risk stratification showed the high-risk group had a poorer prognosis. Conclusion: Predicting clinically relevant risk factors for CRO nosocomial infection and prognosis in elderly patients. This may help the treatment of clinical drug-resistant infections.

scholarly journals Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuyuan Chen ◽  
Changxing Chi ◽  
Dedian Chen ◽  
Sanjun Chen ◽  
Binbin Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Training Set ◽  
Characteristic Curves ◽  
Decision Curve Analysis ◽  
Receiver Operating Characteristic Curves ◽  
Independent Risk Factors

Background. The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients. Methods. Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis. Results. All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates. Conclusion. We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.

scholarly journals Development and Validation of a Nomogram Based on Noninvasive Liver Reserve and Fibrosis (PALBI and FIB-4) Model to Predict Posthepatectomy Liver Failure Grade B-C in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenhui Zhong ◽  
Feng Zhang ◽  
Kaijun Huang ◽  
Yiping Zou ◽  
Yubin Liu
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Liver Failure ◽  
Roc Curves ◽  
Postoperative Liver Failure ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Independent Risk Factors ◽  
Validation Set

Hepatectomy is currently one of the most effective treatments for hepatocellular carcinoma (HCC). However, postoperative liver failure (PHLF) is a serious complication and the leading cause of mortality in patients with HCC after hepatectomy. This study attempted to develop a novel nomogram based on noninvasive liver reserve and fibrosis models, platelet-albumin-bilirubin grade (PALBI) and fibrosis-4 index (FIB-4), able to predict PHLF grade B-C. This was a single-centre retrospective study of 574 patients with HCC undergoing hepatectomy between 2014 and 2018. The independent risk factors of PHLF were screened using univariate and multivariate logistic regression analyses. Multivariate logistic regression was performed using the training set, and the nomogram was developed and visualised. The utility of the model was evaluated in a validation set using the receiver operating characteristic (ROC) curve. A total of 574 HCC patients were included (383 in the training set and 191 for the validation set) and included PHLF grade B-C complications of 14.8, 15.4, and 13.6%, respectively. Overall, cirrhosis ( P < 0.026 , OR = 2.296, 95% confidence interval (CI) 1.1.02–4.786), major hepatectomy ( P = 0.031 , OR = 2.211, 95% CI 1.077–4.542), ascites ( P = 0.014 , OR = 3.588, 95% 1.299–9.913), intraoperative blood loss ( P < 0.001 , OR = 4.683, 95% CI 2.281–9.616), PALBI score >−2.53 (, OR = 3.609, 95% CI 1.486–8.764), and FIB-4 score ≥1.45 ( P < 0.001 , OR = 5.267, 95% CI 2.077–13.351) were identified as independent risk factors associated with PHLF grade B-C in the training set. The areas under the ROC curves for the nomogram model in predicting PHLF grade B-C were significant for both the training and validation sets (0.832 vs 0.803). The proposed nomogram predicted PHLF grade B-C among patients with HCC with a better prognostic accuracy than other currently available fibrosis and noninvasive liver reserve models.

scholarly journals Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System

2022 ◽  
Vol 21 ◽  
pp. 153303382110662
Author(s):  
Zhiyi Fan ◽  
Changxing Chi ◽  
Yuexin Tong ◽  
Zhangheng Huang ◽  
Youxin Song ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Tumor Size ◽  
Training Set ◽  
N Stage ◽  
Independent Risk Factors ◽  
Testing Set

Background: Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM. Materials and Methods: The data of patients were derived from the Surveillance, Epidemiology, and End Results database during the period of 2010 to 2015. Logistic and Cox analysis was performed to determine the independent risk factors and prognostic factors of STS patients with LM, respectively. Afterward, 2 nomograms were, respectively, established based on these factors. The performance of the developed nomogram was evaluated with receiver operating characteristic curves, area under the curve (AUC) calibration curves, and decision curve analysis (DCA). Results: A total of 7643 patients with STS were included in this study. The independent predictors of LM in first-diagnosed STS patients were N stage, grade, histologic type, and tumor size. The independent prognostic factors for STS patients with LM were age, N stage, surgery, and chemotherapy. The AUCs of the diagnostic nomogram were 0.806 in the training set and 0.799 in the testing set. For the prognostic nomogram, the time-dependent AUC values of the training and testing set suggested a favorable performance and discrimination of the nomogram. The 1-, 2-, and 3-year AUC values were 0.698, 0.718, and 0.715 in the training set, and 0.669, 0.612, and 0717 in the testing set, respectively. Furthermore, for the 2 nomograms, calibration curves indicated satisfactory agreement between prediction and actual survival, and DCA indicated its clinical usefulness. Conclusion: In this study, grade, histology, N stage, and tumor size were identified as independent risk factors of LM in STS patients, age, chemotherapy surgery, and N stage were identified as independent prognostic factors of STS patients with LM, these developed nomograms may be an effective tool for accurately predicting the risk and prognosis of newly diagnosed patients with LM.

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