Dataset Information

Nomogram for predicting the overall survival and cancer-specific survival of patients with extremity liposarcoma: a population-based study.

ABSTRACT: BACKGROUND:Extremity liposarcoma represents 25% of extremity soft tissue sarcoma and has a better prognosis than liposarcoma occurring in other anatomic sites. The purpose of this study was to develop two nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity liposarcoma. METHODS:A total of 2170 patients diagnosed with primary extremity liposarcoma between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to explore the independent prognostic factors and establish two nomograms. The area under the curve (AUC), C-index, calibration curve, decision curve analysis (DCA), Kaplan-Meier analysis, and subgroup analyses were used to evaluate the nomograms. RESULTS:Six variables were identified as independent prognostic factors for both OS and CSS. In the training cohort, the AUCs of the OS nomogram were 0.842, 0.841, and 0.823 for predicting 3-, 5-, and 8-year OS, respectively, while the AUCs of the CSS nomogram were 0.889, 0.884, and 0.859 for predicting 3-, 5-, and 8-year CSS, respectively. Calibration plots and DCA revealed that the nomogram had a satisfactory ability to predict OS and CSS. The above results were also observed in the validation cohort. In addition, the C-indices of both nomograms were significantly higher than those of all independent prognostic factors in both the training and validation cohorts. Stratification of the patients into high- and low-risk groups highlighted the differences in prognosis between the two groups in the training and validation cohorts. CONCLUSION:Age, sex, tumor size, grade, M stage, and surgery status were confirmed as independent prognostic variables for both OS and CSS in extremity liposarcoma patients. Two nomograms based on the above variables were established to provide more accurate individual survival predictions for extremity liposarcoma patients and to help physicians make appropriate clinical decisions.

SUBMITTER: Ye L

PROVIDER: S-EPMC7493333 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Nomogram for predicting the overall survival and cancer-specific survival of patients with extremity liposarcoma: a population-based study.

Ye Lin L Hu Chuan C Wang Cailin C Yu Weiyang W Liu Feijun F Chen Zhenzhong Z

BMC cancer 20200916 1

<h4>Background</h4>Extremity liposarcoma represents 25% of extremity soft tissue sarcoma and has a better prognosis than liposarcoma occurring in other anatomic sites. The purpose of this study was to develop two nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity liposarcoma.<h4>Methods</h4>A total of 2170 patients diagnosed with primary extremity liposarcoma between 2004 and 2015 were extracted from the Surveillance, Epidemiology, an ...[more]

PMID: 32938431

Similar Datasets

Project description:BackgroundRenal cell carcinoma (RCC) is a common malignant tumor in the elderly, with an increasing trend in recent years. We aimed to construct a nomogram of cancer-specific survival (CSS) and overall survival (OS) in elderly patients with nonmetastatic renal cell carcinoma (nmRCC).MethodsClinicopathological information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program in elderly patients with nmRCC from 2010 to 2015. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression analyses were used to identify independent risk factors for patient outcomes in the training cohort. A nomogram was constructed based on these independent risk factors to predict the 1-, 3-, and 5-year CSS and OS in elderly patients with nmRCC. We used a range of methods to validate the accuracy and reliability of the model, including the calibration curve, consistency index (C-index), and the area under the receiver operating curve (AUC). Decision curve analysis (DCA) was used to test the clinical utility of the model.ResultsA total of 12,116 patients were enrolled in the study. Patients were randomly assigned to the training cohort (N = 8,514) and validation cohort (N = 3,602). In the training cohort, univariate and multivariate Cox regression analysis showed that age, marriage, tumor histological type, histological tumor grade, TN stage, tumor size, and surgery are independent risk factors for prognosis. A nomogram was constructed based on independent risk factors to predict CSS and OS at 1-, 3-, and 5- years in elderly patients with nmRCC. The C-index of the training and validation cohorts in CSS were 0.826 and 0.831; in OS, they were 0.733 and 0.734, respectively. The AUC results of the training and validation cohort were similar to the C-index. The calibration curve indicated that the observed value is highly consistent with the predicted value, meaning the model has good accuracy. DCA results suggest that the clinical significance of the nomogram is better than that of traditional TNM staging.ConclusionsWe built a nomogram prediction model to predict the 1-, 3- and 5-year CSS and OS of elderly nmRCC patients. This model has good accuracy and discrimination and can help doctors and patients make clinical decisions and active monitoring.

Project description:PurposeThis study aimed to explore a visual model for predicting the prognosis of patients with parathyroid carcinoma (PC) and analyze related biochemistries in different groups of stage.MethodsThe training dataset of 342 patients with PC was obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and the validation dataset included 59 patients from The First Affiliated Hospital of Zhengzhou University. Univariate and multivariate Cox regression analyses were performed to evaluate significant independent prognostic factors. Based on those factors, nomograms and Web-based probability calculators were constructed to evaluate the overall survival (OS) and the cancer-specific survival (CSS) at 3, 5, and 8 years. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. Moreover, biochemistries from the validation set were retrospectively analyzed in different groups of stage by Kruskal-Wallis test.ResultsAge, marital status, tumor size, stage, lymph node status, and radiation were identified as prognostic factors of OS. In contrast, only tumor size and stage were predictive for CSS. The nomogram was developed based on these independent factors. The C-index, ROC curve, calibration curve, and DCA of the nomogram in both training and validation sets showed that the nomogram had good predictive value, stability, and clinical benefit in predicting 3-, 5-, and 8-year OS and CSS in PC patients. Among the 59 PC patients from our hospital, lower albumin (ALB) levels and higher postoperative parathyroid hormone (PTH) levels were found in patients with distant metastasis (Distant vs. Regional ALB levels: p = 0.037; Distant vs. Local ALB levels: p = 0.046; Distant vs. Regional postoperative PTH levels: p = 0.002; Distant vs. Local postoperative PTH: p = 0.002).ConclusionThe established nomogram application can provide accurate prognostics for patients with PC in the Chinese population, but it must be validated on prospectively collected real-world data.

Project description:BackgroundAlthough some studies have explored prognostic factors of myxofibrosarcoma (MFS), the sample sizes were small, generally fewer than 100 patients. There is still no effective prognostic model for MFS patients based on a large population and comprehensive factors. The present study was designed to establish and validate a large population-based, clinically relevant prognostic nomogram for predicting 3- and 5-year overall survival (OS) in patients with MFS.MethodsWe identified patients with MFS (ICD-O-3 code: 8811/3) who were diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database and separated them into training and validation cohorts (7:3 ratio). Survival was described using the Kaplan-Meier method. Univariate and multivariate Cox regression analyses were used to identify prognostic factors of survival. An individual nomogram was established to predict OS at 3 and 5 years in MFS patients. The discriminative ability and predictive accuracy of the nomogram were compared to those of the traditional American Joint Committee on Cancer (AJCC) staging system in the training and validation cohorts. Finally, MFS patients were divided into two subgroups based on the prognostic index (PI) score of the nomogram, and the survival outcomes of the subgroups were compared.ResultsA total of 1,270 patients were included. Age at diagnosis, total number of in situ or malignant tumors, tumor size, tumor site, tumor extension, AJCC stage, surgical status, chemotherapy, and radiotherapy were the independent predictors of survival and were included in the nomogram. The nomogram had C-indexes of 0.806 in the training cohort and 0.783 in the validation cohort, which were greater than those of the sixth edition of the AJCC staging system (training cohort, 0.669 and validation cohort, 0.674). Decision curve analysis (DCA) revealed that the nomogram was useful with high clinical net benefits. Survival outcomes were significantly different between the different risk subgroups (P<0.001).ConclusionsA novel nomogram based on a large population was constructed to evaluate survival outcomes for MFS. Its predictive efficacy was markedly superior than that of the traditional sixth edition of the AJCC staging system.

Project description:BackgroundProstate cancer (PC) is the second most common malignant tumor, and its survival is of great concern. However, the assessment of survival risk in current studies is limited. This study is to develop and validate a nomogram for the prediction of survival in PC patients using data from the Surveillance, Epidemiology, and End Results (SEER) database.MethodsA total of 153,796 PC patients were included in this cohort study. Patients were divided into a training set (n=107,657) and a testing set (n=46,139). The 3-, 5- and 10-year survival of the PC patients were regarded as the outcomes. Predictors based on the demographic and pathological data for survival were identified by multivariate Cox regression analysis to develop the predictive nomogram. Internal and subgroup validations were performed to assess the predictive performance of the nomogram. The C-index, time-dependent receiver operating characteristic (ROC) curves, and corresponding areas under the ROC curves (AUCs) were used to estimate the predictive performance of the nomogram.ResultsAge at diagnosis, race, marital status, tumor node metastasis (TNM) stage, prostate specific antigen (PSA) status, Gleason score, and pathological stage were identified as significantly associated with the survival of PC patients (P<0.05). The C-index of the nomogram indicated a moderate predictive ability [training set: C-index =0.782, 95% confidence interval (CI): 0.779-0.785; testing set: C-index =0.782, 95% CI: 0.777-0.787]. The AUCs of this nomogram for the 3-, 5-, and 10-year survival were 0.757 (95% CI: 0.756-0.758), 0.741 (95% CI: 0.740-0.742), and 0.716 (95% CI: 0.715-0.717), respectively. The results of subgroup validation showed that all the AUCs for the nomogram at 3, 5, and 10 years were more than 0.70, regardless of marital status and race.ConclusionsWe developed a nomogram with the moderate predictive ability for the long-term survival (3-, 5-, and 10-year survival) of patients with PC.

Dataset Information

Nomogram for predicting the overall survival and cancer-specific survival of patients with extremity liposarcoma: a population-based study.

Publications

Nomogram for predicting the overall survival and cancer-specific survival of patients with extremity liposarcoma: a population-based study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets