Nomogram application to predict overall and cancer-specific survival in osteosarcoma

Zheng, Weipeng; Huang, Yuanping; Chen, Haoyi; Wang, Ning; Xiao, Wende; Liang, Yao; Jiang, Xin; Su, Wenzhou; Wen, Shimin

doi:10.2147/cmar.s177945

Cited by 53 publications

(70 citation statements)

References 40 publications

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“…Wong et al reported that the range of variables considered is usually determined based on data availability and clinical evidence rather than on statistical signi cance (14). Therefore, to predict prognosis precisely, we constructed nomograms based on a combination of four independent risk factors (patient age, histology, grade and surgery) that were identi ed as signi cant independent risk factors associated with both OS and CCS in univariate and multivariate Cox proportional hazards regression analyses from 13 variables.Most studies have demonstrated that tumor size is also a risk factor for the overall and cancer-speci c survival prognosis of patients with spinal tumors (4,5,15,16). In our study, the size of the tumor was not associated with a statistically signi cant decrease in OS and CCS prognosis in the multivariate Cox analysis.…”

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confidence: 56%

Development and Validation of Nomograms Predicting Overall and Cancer-specific Survival of Spinal and Pelvic Tumor Patients with Distant Metastasis

Zhang¹,

Tu²,

Zhou³

et al. 2020

Preprint

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Background: Primary spinal bone tumors with distant metastasis are a sign of advanced stage and are usually accompanied by poor prognosis. This study is to identify the risk factors and establish prognostic nomograms to predict 1- and 3-year overall survival (OS) and cancer-specific survival (CSS) rates for spinal and pelvic bone tumor patients with distant metastasis.Patients and methods: Spinal and pelvic bone tumor patients with distant metastasis between 1998 and 2016 were selected for this study from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms to predict 1- and 3-year OS and CCS rates were constructed based on independent risk factors identified by univariate and multivariate Cox analyses. Concordance indexes (C-indexes), receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were used to assess the nomograms.Results: All patients (n=343) were randomly divided into a training cohort (n=243) and validation cohort (n=100). No significant differences were found in thedemographic data of all patients in the training and validation cohorts. Ultimately, only four independent risk factors (patient age, histology, grade and surgery) were identified as significantly associated with OS and CCS. The C-indices were 0.722 (95% CI, 0.685 to 0.759) and 0.686 (95% CI, 0.61 to 0.760) for the internal validation and external validation of the OS nomogram, respectively. Similarly, the C-indices based on the CCS nomogram were 0.717 (95% CI, 0.678 to 0.757) and 0.695 (95% CI, 0.619 to 0.771) for the internal validation and external validation, respectively. The calibration curves revealed that the predicted survival and actual survival were in concordance. DCA showed the clinical utility and benefits of the nomograms.Conclusion: The nomograms we constructed based on the SEER database can accurately predict individual patient survival.

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contrasting

confidence: 56%

Development and Validation of Nomograms Predicting Overall and Cancer-specific Survival of Spinal and Pelvic Tumor Patients with Distant Metastasis

Zhang¹,

Tu²,

Zhou³

et al. 2020

Preprint

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show abstract

“…Nomograms, statistic-based tools integrating all independent prognostic factors, have been widely applied to predict survival outcomes with precision for individual patients with cancer, including osteosarcoma, lung cancer, rectal cancer, and gastric cancer. [8][9][10][11] However, to our knowledge, the application of prognostic nomograms for pelvic CS has not been reported. The Surveillance, Epidemiology, and End Results (SEER) database released a large amount of clinical information about patients with pelvic CS that allowed prognostic analysis for pelvic CS.…”

Section: Introductionmentioning

confidence: 98%

“…Nomograms, statistic‐based tools integrating all independent prognostic factors, have been widely applied to predict survival outcomes with precision for individual patients with cancer, including osteosarcoma, lung cancer, rectal cancer, and gastric cancer . However, to our knowledge, the application of prognostic nomograms for pelvic CS has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Prognostic nomograms to predict overall survival and cancer‐specific survival in patients with pelvic chondrosarcoma

et al. 2019

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Background The pelvis is the most common site of chondrosarcoma (CS), and the prognosis for patients with pelvic CS is worse than that for patients with CS in the extremities. However, clinicians have had few tools for estimating the likelihood of survival in patients with pelvic CS. Our aim was to develop nomograms to predict survival of patients with pelvic CS. Methods Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with pelvic CS between 2004 and 2016 were retrieved for retrospective analysis. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. On the basis of the results of the multivariate analyses, nomograms were constructed to predict the likelihood of 3‐ and 5‐year overall survival (OS) and cancer‐specific survival (CSS) of patients with pelvic CS. The concordance index (C‐index) and calibration curves were used to test the models. Results In univariate and multivariate analyses of OS, sex, pathologic grade, tumor size, tumor stage, and surgery were identified as the independent risk factors. In univariate and multivariate analyses of CSS, pathologic grade, tumor size, tumor stage, and surgery were identified as the independent risk factors. These characteristics except surgery were integrated in the nomograms for predicting 3‐ and 5‐year OS and CSS, and the C‐indexes were 0.758 and 0.786, respectively. Conclusion The nomograms precisely and individually predict OS and CSS of patients with pelvic CS and could aid in personalized prognostic evaluation and individualized clinical decision‐making.

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“…Nomogram, statistics‐based tool aggregating several independent risk factors into an intuitive graph, has been widely used in recent years to assess the prognosis for many types of cancer . Therefore, this study aimed to develop effective prognostic nomograms and a new scoring system based on a large data set to predict overall survival (OS) and cancer‐specific survival (CSS) of patients with kidney cancer to help clinicians provide personalized treatment recommendations.…”

Section: Introductionmentioning

confidence: 99%

Prognostic nomograms and Aggtrmmns scoring system for predicting overall survival and cancer‐specific survival of patients with kidney cancer

Zhou¹,

Zhang²,

Ding³

et al. 2020

Cancer Medicine

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Background: Currently, the prognosis of kidney cancer depends mainly on the pathological grade or tumor stage. Clinicians have few effective tools that can personalize and adequately evaluate the prognosis of kidney cancer patients. Methods: A total of 70 481 kidney cancer patients were selected from the Surveillance, Epidemiology, and End Results database, among which patients diagnosed in 2005-2011 (n = 42 890) were used to establish nomograms for overall survival (OS) and cancer-specific survival (CSS), and those diagnosed in 2012-2015 (n = 24 591) were used for external validation. Univariate and multivariate Cox analyses were used to determine independent prognostic factors. Concordance index (C-index), receiver operating characteristic curve, and calibration curve were used to evaluate the predictive capacity of the nomograms. We further reduced subgroup classification and used propensity score matching to balance clinical informations, and analyzed the effect of other variables on survival. We established a new kidney cancer prognostic score system based on the effect of all available variables on survival. Cox proportional hazard model and Kaplan-Meier curves were used for survival comparison. Results: Age, gender, marital status, surgery, grade, T stage, and M stage were included as independent risk factors in the nomograms. The favorable area under the curve (AUC) value (for OS, AUC = 0.812-0.858; and for CSS, AUC = 0.890-0.921), internal (for OS, C-index = 0.776; and for CSS, C-index = 0.856), and external (for OS, C-index = 0.814-0.841; and for CSS, C-index = 0.894-0.904) validation indi-cated that the proposed nomograms could accurately predict 1-, 3-, and 5-year OS and CSS of kidney cancer patients. The Aggtrmmns prognostic scoring system based on age, gender, race, marital status, grade, TNM stage, and surgery of kidney cancer patients could stage patients more explicitly than the AJCC staging system.

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Nomogram application to predict overall and cancer-specific survival in osteosarcoma

Cited by 53 publications

References 40 publications

Development and Validation of Nomograms Predicting Overall and Cancer-specific Survival of Spinal and Pelvic Tumor Patients with Distant Metastasis

Development and Validation of Nomograms Predicting Overall and Cancer-specific Survival of Spinal and Pelvic Tumor Patients with Distant Metastasis

Prognostic nomograms to predict overall survival and cancer‐specific survival in patients with pelvic chondrosarcoma

Prognostic nomograms and Aggtrmmns scoring system for predicting overall survival and cancer‐specific survival of patients with kidney cancer

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