scholarly journals Log Odds of Positive Lymph Node- (LODDS-) Based Competing-Risk Nomogram for Predicting Prognosis of Resected Rectal Cancer: A Development and Validation Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Rui-zhe Zheng ◽  
Jiang Xie ◽  
Shui-qiang Zhang ◽  
Wen Li ◽  
Bo Dong ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Death Rate ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
Tnm Stage ◽  
Competing Risk ◽  
Brier Score ◽  
Log Odds

Background and Aims. Cancer-specific survival (CSS) of rectal cancer (RC) is associated with several factors. We aimed to build an efficient competing-risk nomogram based on log odds of positive lymph nodes (LODDS) to predict RC survival. Methods. Medical records of 8754 patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database, of 4895 patients from SEER during 2011–2014 and of 478 patients from an Eastern center as a development cohort, validation cohort, and test cohort, respectively. Univariate and multivariate competing-risk analyses were performed to build competing-risk nomogram for predicting the CSS of RC patients. Prediction efficacy was evaluated and compared with reference to the 8th TNM classification using the factor areas under the receiver operating characteristic curve (AUC) and Brier score. Results. The competing-risk nomogram was based on 6 variables: size, M stage, LODDS, T stage, grade, and age. The competing-risk nomogram showed a higher AUC value in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.81 vs. 0.76), validation cohort (0.85 vs. 0.82), and test cohort (0.71 vs. 0.66). The competing-risk nomogram also showed a higher Brier score in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.120 vs. 0.127), validation cohort (0.123 vs. 0.128), and test cohort (0.202 vs. 0.226). Conclusion. We developed and validated a competing-risk nomogram for RC death, which could provide the probability of survival averting competing risk to facilitate clinical decision-making.

scholarly journals A Modified TNM Classification for Primary Operable Colorectal Cancer

2020 ◽  
Author(s):  
Chundong Zhang ◽  
Zubing Mei ◽  
Junpeng Pei ◽  
Masanobu Abe ◽  
Xiantao Zeng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Validation Cohort ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Fitting ◽  
Information Criteria ◽  
Stage I ◽  
Internal Validation ◽  
Clinical Benefits

Abstract Background The American Joint Committee on Cancer (AJCC) 8th tumor/node/metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods We included 45,379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (N =31,772) or an internal validation cohort (N =13,607). External validation was performed in 10,902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve (AUC) and Akaike information criteria (AIC) were applied for prognostic discrimination and model-fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a), IIA (T1N1b, T2N1b, T3N0), IIB (T1-2N2a-2b, T3N1a-1b, T4aN0), IIC (T3N2a, T4aN1a-2a, T4bN0), IIIA (T3N2b, T4bN1a), IIIB (T4aN2b, T4bN1b), and IIIC (T4bN2a-2b). In the internal validation cohort, compared to the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (AUC = 0.675 vs. 0.667, respectively; two-sided P <0.001) and better model-fitting (AIC = 70,937 vs. 71,238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.

scholarly journals Application of Thromboelastography to Predict Lung Cancer Stage

2020 ◽  
Vol 19 ◽  
pp. 153303382095235
Author(s):  
Yaning Zhou ◽  
Yijun Guo ◽  
Qing Cui ◽  
Yun Dong ◽  
Xiaoyue Cai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Maximum Amplitude ◽  
Stage Iv ◽  
Tnm Stage ◽  
Stage I ◽  
Stage Iii ◽  
Α Angle

Objective: Lung cancer is often associated with hypercoagulability. Thromboelastography provides integrated information on clot formation in whole blood. This study explored the possible relationship between thromboelastography and lung cancer. Methods: Lung cancer was staged according to the Tumor, Node, and Metastasis (TNM) classification system. Thromboelastography parameters in different stages of disease were compared. The value of thromboelastography for stage prediction was determined by area under the receiver operating characteristic curve analysis. Results: A total of 182 patients diagnosed with lung cancer were included. Thromboelastography parameters, including kinetics time, α-angle, and maximum amplitude, differed significantly between patients with metastatic and limited lung cancers ( P < 0.05). Kinetics time was significantly reduced and maximum amplitude was significantly increased in patients with stage I and II compared with stage III and IV tumors ( P < 0.05). TNM stage was significantly negatively correlated with kinetics time ( r = −0.186), and significantly positively correlated with α-angle ( r = 0.151) and maximum amplitude ( r = 0.251) (both P < 0.05). The area under the curve for kinetics time in patients with stage I cancer was 0.637 ( P < 0.05) and that for α-angle in stage ≥ II was 0.623 ( P < 0.05). The areas under the curves for maximum amplitude in stage ≥ III and stage IV cancer were 0.650 and 0.605, respectively (both P < 0.05). Thromboelastography parameters were more closely associated with TNM stage in patients with lung adenocarcinoma than in the whole lung cancer population. Conclusion: This study identified the diagnostic value of thromboelastography parameters for determining tumor stage in patients with lung cancer. Thromboelastography can be used as an independent predictive parameter for lung cancer severity.

scholarly journals Development and validation of nomogram combining serum biomarker for predicting survival in patients with resected rectal cancer

2019 ◽  
Vol 39 (11) ◽  
Author(s):  
Shaonan Fan ◽  
Ting Li ◽  
Ping Zhou ◽  
Qiliang Peng ◽  
Yaqun Zhu
Keyword(s):  
Rectal Cancer ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Staging System ◽  
Clinical Decision ◽  
Tnm Staging ◽  
P Value ◽  
Training Cohort ◽  
American Joint Committee

Abstract Purpose: Nomogram is a widely used tool that precisely predicts individualized cancer prognoses. We aimed to develop and validate a reliable nomogram including serum tumor biomarkers to predict individual overall survival (OS) for patients with resected rectal cancer (RC) and compare the predictive value with the American Joint Committee on Cancer (AJCC) stages. Patients and methods: We analyzed 520 patients who were diagnosed with non-metastatic rectal cancer as training cohort. External validation was performed in a cohort of 11851 patients from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified and integrated to build a nomogram using the Cox proportional hazard regression model. The nomogram was evaluated by Harrell’s concordance index (C-index) and calibration plots in both training and validation cohort. Results: The calibration curves for probability of 1-, 3-, and 5-year OS in both cohorts showed favorable accordance between the nomogram prediction and the actual observation. The C-indices of the nomograms to predict OS were 0.71 in training cohort and 0.69 in the SEER cohort, which were higher than that of the seventh edition American Joint Committee on Cancer TNM staging system for predicting OS (training cohort, 0.71 vs. 0.58, respectively; P-value &lt; 0.001; validation cohort, 0.69 vs. 0.57, respectively; P-value &lt; 0.001). Conclusion: We developed and validated a novel nomogram based on CEA and other factors for predicting OS in patients with resected RC, which could assist clinical decision making and improvement of prognosis prediction for individual RC patients after surgery.

Abstract TMP85: Predictive Score of Hemorrhagic Transformation in Patients Not Submitted to Reperfusion Therapies - PROpHET

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Joao B Andrade ◽  
Gisele S Silva ◽  
Jay P Mohr ◽  
Joao J Carvalho ◽  
Luisa Franciscatto ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Reperfusion Therapy ◽  
Predictive Score ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Derivation Cohort ◽  
Digital Version

Objective: To create an accurate and user-friendly pr edictive sc o re for he morrhagic t ransformation in patients not submitted to reperfusion therapies (PROpHET). Methods: We created a multivariable logistic regression model to assess the prediction of Hemorrhage Transformation (HT) for acute ischemic strokes not treated with reperfusion therapy. One point was assigned for each of gender, cardio-aortic embolism, hyperdense middle cerebral artery sign, leukoaraiosis, hyperglycemia, 2 points for ASPECTS ≤7, and -3 points for lacunar syndrome. Acute ischemic stroke patients admitted to the Fortaleza Comprehensive Stroke Center in Brazil from 2015 to 2017 were randomly selected to the derivation cohort. The validation cohort included similar, but not randomized, cases from 5 Brazilian and one American Comprehensive Stroke Centers. Symptomatic cases were defined as NIHSS ≥4 at 24 hours after the event. Results from the derivation and validation cohorts were assessed with the area under the receiver operating characteristic curve (AUC-ROC). Results: From 2,432 of acute ischemic stroke screened in Fortaleza, 448 were prospectively selected for the derivation cohort and a 7-day follow-up. From 1,847 not selected, 577 underwent reperfusion therapy, 734 were excluded due to inadequate imaging or refusal of consent, and 538 whose data were obtained retrospectively and were selected only for the validation cohort. A score ≥3 had 78% sensitivity and 75% specificity, AUC-ROC 0.82 for all cases of HT, Hosmer-Lemeshow 0.85, Brier Score 0.1, and AUC-ROC 0.83 for those with symptomatic HT. An AUC-ROC of 0.84 was found for the validation cohort of 1,910 from all 6 centers, and a score ≥3 was found in 65% of patients with HT against 11.3% of those without HT. In comparison with 8 published predictive scores of HT, PROpHET was the most accurate (p < 0.01). Conclusions: PROpHET offers a tool simple, quick and easy-to-perform to estimate risk stratification of HT in patients not submitted to RT. A digital version of PROpHET is available in www.score-prophet.com Classification of evidence: This study provides Class I evidence from prospective data acquisition.

scholarly journals Development and validation of the RCOS prognostic index: a bedside multivariable logistic regression model to predict hypoxaemia or death in patients with of SARS-CoV-2 infection

2021 ◽  
Author(s):  
Gerardo Alvarez-Uria ◽  
Sumanth Gandra ◽  
Venkata R Gurram ◽  
Raghu P Reddy ◽  
Manoranjan Midde ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Prognostic Index ◽  
Prognostic Scores ◽  
Prognostic Models ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Neutrophil Lymphocyte Ratio ◽  
Development And Validation

Previous COVID-19 prognostic models have been developed in hospital settings, and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis. The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death. 4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892-0.922) and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925-0.97) and the Brier score was 0.0188. If our results are validated in other settings, the RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource limited-settings.

scholarly journals The Prognostic Ability of Staging System in Men with Penile Cancer: An Analysis of SEER Database

2020 ◽  
Vol 4 (1) ◽  
pp. 68-77
Author(s):  
Aditya Jalan ◽  
Ravi Kanodia ◽  
Sarita Rana Gurung ◽  
Rajeev Kumar Malhotra ◽  
Umesh Nepal ◽  
...  
Keyword(s):  
Penile Cancer ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
Rare Condition ◽  
Tnm Stage ◽  
Prognostic Models ◽  
Ajcc Stage ◽  
Free Rate

Background: Penile cancer is now a rare condition. The low incidence of the disease makes a valid estimation of its prognosis difficult. In this study, we made an attempt and propose a nomogram to develop a prognostic rule that could predict the Cancer-Specific Mortality (CSM) free rates in patients with primary penile squalors cell carcinoma of the penis (PPSCC).Methods: This study included 1304 patients diagnosed with PPSCC between the years 2004 & 2011 and treated with penile tumor excision. Subjects were staged as per Surveillance, Epidemiology & End Results stage (SEER), American Joint Committee on Cancer (AJCC), TNM classification and tumor grade (TG). CSM free rates were determined. Univariate and multivariate Cox regression model was used to test the prediction of the CSM free rate. The predictive rule accuracy was created using the receiver operating characteristic curve. Results: The clinico-pathological profile depicts a mean age of 64.66 ± 14.38 yrs. The most common primary site involved was glans penis (n= 483, 37%) and the disease was most commonly diagnosed at AJCC stage I (n= 670, 51.4%) disease. The cumulative 5-year CSM free rates according to Fine & Gray, & Kaplan-Meier methods were 81.8% and 79.8%, respectively. The predictive accuracy as per SEER stage, AJCC stage, TNM stage alone were 68.8%, 70.3%, 72.3%, respectively. When TG was combined, the predictive accuracy increased to 72.8%, 73.1%, and 75.0%, respectively. TNM stage with TG was most accurate in predicting CSM free rate compared to other models. Conclusions: TNM stage with TG and AJCC stage with TG appear to have comparable accuracy to predict the CSM free rate in patients with PPSCC, the TNM stage with TG is the most accurate (75%) method to predict the CSM free rates. The addition of the TG variable improved the accuracy of these prognostic models.

scholarly journals Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245281
Author(s):  
Bianca Magro ◽  
Valentina Zuccaro ◽  
Luca Novelli ◽  
Lorenzo Zileri ◽  
Ciro Celsa ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Brier Score ◽  
Clinical Prediction ◽  
Duration Of Symptoms ◽  
Derivation Cohort ◽  
Independent Risk Factors

Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95%CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease (HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.

scholarly journals Development of A Novel Competing Risk Survival Predicting Model for Esophagogastric Junction Adenocarcinoma: a SEER population-based study

2020 ◽  
Author(s):  
Tongbo Wang ◽  
Yan Wu ◽  
Hong Zhou ◽  
Chaorui Wu ◽  
Xiaojie Zhang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Esophagogastric Junction ◽  
Tnm Classification ◽  
Population Based ◽  
Tnm Staging ◽  
Competing Risk ◽  
Brier Score ◽  
Clinicopathological Feature ◽  
Seer Database ◽  
Esophagogastric Junction Adenocarcinoma

Abstract Background: Adenocarcinoma in Esophagogastric Junction (AEG) is a severe gastrointestinal malignancy with a unique clinicopathological feature. To develop a competing risk nomogram for AEG patients and compared it with new 8th traditional tumor-node-metastasis (TNM) staging system.Methods: Based on AEG patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2010, we used the univariate and multivariate analysis to filter clinical factors and then built the competing risk nomogram to predicting the AEG cause-specific survival. We measured the clinical accuracy by comparing to the 8th TNM stage with receiver operating characteristic (ROC) curve, Brier score, and decision curve analysis (DCA).Results: Total of 1755 patients were included into this study. This nomogram was based on five variables: Number of examined lymph nodes, grade, invasion, metastatic LNs and age. The nomogram model was greater than traditional TNM staging with ROC curve (1-year AUC:0.747 vs. 0.641, 3-year AUC: 0.761 vs. 0.679, 5-year AUC: 0.759 vs. 0.682, 7-year AUC: 0.749 vs. 0.673, P<0.001), Brier score (3-year: 0.198 vs. 0.217, P=0.012; 5-year: 0.198 vs. 0.216, P=0.008; 7-year: 0.199 vs. 0.215, P=0.014) and DCA.Conclusions: Based on the SEER database with AEG patients, the competing risk nomogram showed the greater accurate individualized prediction of the survival compared with traditional TNM classification.

scholarly journals A Prognostic Model Constructed Based on Rna Binding Protein to Predict the Prognosis of Patients With Rectal Cancer

2021 ◽  
Author(s):  
Liqiang Zhou ◽  
You Wu ◽  
Shihao Li ◽  
Dengzhong Wu ◽  
Jinliang Wang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Roc Curve ◽  
Prognostic Model ◽  
Clinical Decision Making ◽  
Rna Binding ◽  
Validation Cohort ◽  
Rna Binding Protein ◽  
Clinical Decision ◽  
Clinical Samples ◽  
Training Cohort

Abstract Background: The incidence of rectal cancer in young people is increasing, and there has been a problem of poor prognosis in recent years. Many studies have shown that RNA binding protein (RBP) is related to the progression of various malignant tumors. However, the role of RBPs in rectal cancer is poorly understood. New prognostic models are urgently needed.Materials and methods: In the study, we used the RBPTD database, The Cancer Genome Atlas (TCGA) database and the transcription data information and corresponding clinical information of rectal cancer patients in the Gene Expression Omnibus (GEO) database to screen out RBPs that are differentially expressed in tumor tissues and normal tissues. Subsequently, we analyzed the prognostic value of these RBPs using bioinformatics methods. In order to screen the key RBP in the occurrence of rectal tumors and establish a prognostic risk score model. The use of survival analysis shows that assessing the relationship between key RBPs and the patient's overall survival rate. In the TCGA cohort, the prognostic model was further tested. At the same time, the nomogram of the 6 RBP mRNAs in the TCGA cohort was constructed, and the ROC curve was used for verification. Finally, q-PCR was performed on clinical samples to verify the expression of hub genes.Results: The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good results in the training cohort and validation cohort. The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good survival prediction in both the training cohort and the validation cohort. The constructed nomogram has certain guiding significance for clinical decision-making. In addition, GSEA analysis revealed potential biological functions. The q-PCR verification results showed the consistency with the construction of the prognostic model.Conclusions: We constructed a six RBPs prognostic model and a nomogram to predict the prognosis of patients with rectal cancer, and performed q-PCR expression testing through clinical samples, which may help clinical decision-making.

scholarly journals How to Predict Cancer-Specific Death of Patients with Chondrosarcoma: A Prognostic Model Based on Competing Risk

2021 ◽  
Author(s):  
Binxiang Zhu ◽  
Yinmin Dong ◽  
Hongyu Zhu ◽  
Zijian Dong ◽  
Feng Li
Keyword(s):  
Prognostic Model ◽  
Risk Model ◽  
Current Model ◽  
Characteristic Curve ◽  
Radical Resection ◽  
Competing Risk ◽  
Brier Score ◽  
Calibration Plot ◽  
Plot Area ◽  
Probability Of Cancer

Abstract Background. As chondrosarcoma is the second highest primary malignant tumor of bone, it is necessary to find a way to predict the prognosis of chondrosarcoma. But the current model rarely involves the study of competing risk. This is a retrospective study with the aim of establishing a prognostic model and a nomogram based on competing risk to predict the probability of cancer-specific death (CSD) at 3 and 5 years. The Fine and Gray regression is a targeted statistical method, which makes the results more authentic and reliable.Methods. A total of 1674 chondrosarcoma patients were identified from the SEER database, and they were divided into training cohort and validation cohort by year of diagnosis. These two cohorts were used to develop and validate the prognostic model to predict the 3-year and 5-year probabilities of CSD, with non-CSD as the competing risk. Model accuracy made use of some verification functions, such as C-index, receiver operating characteristic curve (ROC), calibration plot, area under curve (AUC) and Brier score.Results. According to the outcomes of the model: older age (subdistribution hazards ratio(95%CI): 1.02 (1.01-1.03); P<0.001), dedifferentiated CHS (SHR(95%CI): 2.16 (1.30-3.59); P=0.003), high grade (SHR(95%CI): 2.60 (1.83-3.68); P<0.001), Regional involvement (SHR(95%CI): 3.15 (2.01-4.93); P<0.001), Distant metastasis (SHR(95%CI): 11.56 (6.82-19.59); P<0.001), tumor excision (SHR(95%CI): 0.47 (0.25-0.87); P=0.02) and Radical resection (SHR(95%CI): 0.54 (0.32-0.90); P=0.02) were significantly. They obviously promoted the increase of CSD.Conclusion. This prognostic model considered the competing risks of chondrosarcoma, and the nomogram can effectively predict the probability of CSD in patients with chondrosarcoma, which is suitable for clinical application.

Sign in / Sign up

Export Citation Format

Share Document