Development and Validation of Nomograms Predicting Overall and Cancer-Specific Survival of Spinal Chondrosarcoma Patients

Development and validation of a nomogram integrating marital status for 5-year overall survival of chondrosarcoma: a population-based study

OBJECTIVES

The objective of this study was to evaluate the influence of marital status on overall survival (OS) and develop a nomogram for predicting 5-year OS in chondrosarcoma (CHS) patients.

METHODS

We utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify CHS patients diagnosed between 2010 and 2018. Survival rates were calculated using Kaplan-Meier analysis. Prognostic factors were identified through univariate and multivariate analyses. An independent cohort was used for external validation of the nomogram. Performance evaluation of the nomogram was conducted using Harrell's concordance index (C-index), calibration plot, and decision curve analysis (DCA).

RESULTS

In the SEER cohort, Kaplan-Meier analysis showed significant differences in OS among CHS patients with different marital statuses (P < 0.001), with widowed patients having the lowest OS. In terms of gender, there were significant survival differences based on marital status in females (P < 0.001), but not in males (P = 0.067). The OS of married and single females is significantly higher than that of married (P < 0.001) and single male (P = 0.006), respectively. Kaplan-Meier curves showed no significant difference in OS between groups stratified by either gender or marital status in the external cohort. Univariate and multivariate analyses confirmed that age at diagnosis, gender, marital status, tumor size, histological type, tumor grade, SEER stage, and surgery were independent prognostic factors for OS. The nomogram demonstrated high internal and external validation C-indexes of 0.818 and 0.88, respectively. Calibration plots, DCA curve, and Kaplan-Meier curve (P < 0.001) confirmed the excellent performance and clinical utility of the nomogram.

CONCLUSIONS

Marital status was an independent factor influencing OS in CHS patients, with widowed patients having the worst prognosis. The OS of both married and single females is significantly higher than that of their male counterparts. However, these findings require further validation in a large independent cohort. While the contribution of marital status on predicting OS appears modest, our nomogram accurately predicted 5-year OS and identified high-risk groups, providing a valuable tool for clinical decision-making.

Construction of novel predictive tools for post-surgical cancer-specific survival probability in patients with primary chondrosarcoma and external validation in Chinese cohorts: a large population-based retrospective study

BACKGROUND

Surgery is the predominant treatment modality for chondrosarcoma. This study aims to construct a novel clinic predictive tool that accurately predicts the 3-, 5-, and 8-year probability of cancer-specific survival (CSS) for primary chondrosarcoma patients who have undergone surgical treatment.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 982 primary chondrosarcoma patients after surgery, who were randomly divided into two sets: training set (60%) and internal validation set (40%). Cox proportional regression analyses were used to screen post-surgical independent prognostic variables in primary chondrosarcoma patients. These identified variables were used to construct a nomogram to predict the probability of post-surgical CSS of primary chondrosarcoma patients. The k-fold cross-validation method (k = 10), Harrell's concordance index (C-index), receiver operating characteristic curve (ROC) and area under curve (AUC) were used to assess the predictive accuracy of the nomogram. Calibration curve and decision curve analysis (DCA) were used to validate the clinical application of the nomogram.

RESULTS

Age, tumor size, disease stage and histological type were finally identified post-surgical independent prognostic variables. Based the above variables, a nomogram was constructed to predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. The results of the C-index showed excellent predictive performance of the nomogram (training set: 0.837, 95% CI: 0.766-0.908; internal validation set: 0.835, 95% CI: 0.733-0.937; external validation set: 0.869, 95% CI: 0.740-0.998). The AUCs of ROC were all greater than 0.830 which again indicated that the nomogram had excellent predictive performance. The results of calibration curve and DCA indicated that the clinical applicability of this nomogram was outstanding. Finally, the risk classification system and online access version of the nomogram was developed.

CONCLUSION

We constructed the first nomogram to accurately predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. This nomogram would assist surgeons to provide individualized post-surgical survival predictions and clinical strategies for primary chondrosarcoma patients.

Establishment and validation of a competitive risk model for predicting cancer-specific survival in patients with osteosarcoma: a population-based study

BACKGROUND

Osteosarcoma is the most common primary bone tumor with a poor prognosis. The aim of this study was to establish a competitive risk model nomogram to predict cancer-specific survival in patients with osteosarcoma.

METHODS

Patient data was obtained from the Surveillance, Epidemiology, and End Results database in the United States. A sub-distribution proportional hazards model was used to analyze independent risk factors affecting cancer-specific mortality (CSM) in osteosarcoma patients. Based on these risk factors, a competitive risk model was constructed to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) in osteosarcoma patients. The reliability and accuracy of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves.

RESULTS

A total of 2900 osteosarcoma patients were included. The analysis showed that age, primary tumor site, M stage, surgery, chemotherapy, and median household income were independent risk factors influencing CSM in patients. The competitive risk model was constructed to predict CSS in osteosarcoma patients. In the training and validation sets, the C-index of the model was 0.756 (95% CI 0.725-0.787) and 0.737 (95% CI 0.717-0.757), respectively, and the AUC was greater than 0.7 for both. The calibration curves also demonstrated a high consistency between the predicted survival rates and the actual survival rates, confirming the accuracy and reliability of the model.

CONCLUSION

We established a competitive risk model to predict 1-year, 3-year, and 5-year CSS in osteosarcoma patients. The model demonstrated good predictive performance and can assist clinicians and patients in making clinical decisions and formulating follow-up strategies.

A practical nomogram and risk stratification system predicting the cancer-specific survival for patients aged >50 with advanced melanoma

Objective

To investigate risk factors for advanced melanoma over 50 years of age and to develop and validate a new line chart and classification system.

Methods

The SEER database was screened for patients diagnosed with advanced melanoma from 2010 to 2019 and Cox regression analysis was applied to select variables affecting patient prognosis. The area under curve (AUC), relative operating characteristic curve (ROC), Consistency index (C-index), decision curve analysis (DCA), and survival calibration curves were used to verify the accuracy and utility of the model and to compare it with traditional AJCC tumor staging. The Kaplan-Meier curve was applied to compare the risk stratification between the model and traditional AJCC tumor staging.

Results

A total of 5166 patients were included in the study. Surgery, age, gender, tumor thickness, ulceration, the number of primary melanomas, M stage and N stage were the independent prognostic factors of CSS in patients with advanced melanoma (P<0.05). The predictive nomogram model was constructed and validated. The C-index values obtained from the training and validation cohorts were 0.732 (95%CI: 0.717-0.742) and 0.741 (95%CI: 0.732-0.751). Based on the observation and analysis results of the ROC curve, survival calibration curve, NRI, and IDI, the constructed prognosis model can accurately predict the prognosis of advanced melanoma and performs well in internal verification. The DCA curve verifies the practicability of the model. Compared with the traditional AJCC staging, the risk stratification in the model has a better identification ability for patients in different risk groups.

Conclusion

The nomogram of advanced melanoma and the new classification system were successfully established and verified, which can provide a practical tool for individualized clinical management of patients.

Development and validation of a nomogram for predicting prognosis of high-grade chondrosarcoma: A surveillance, epidemiology, and end results-based population analysis

BACKGROUND

The incidence of chondrosarcoma is increasing every year, and the treatment and prognosis of patients with high-grade chondrosarcoma are becoming more and more important. Nomogram is a tool that can quickly and easily predict the overall survival of tumor patients. Therefore, the development and validation of a nomogram to predict overall survival in patients with high-grade chondrosarcoma was desired.

METHODS

We retrospectively collected 396 patients with high-grade chondrosarcoma from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Randomly divided into model and validation groups, the best cut-off values for age and tumor size grouping were derived by using X-tile software. Then, independent prognostic factors for high-grade chondrosarcoma were derived by SPSS.26 univariate and multivariate Cox analyses analysis in the model group, and the model was evaluated by using R software, using C-indix and ROC curves, and finally these independent prognostic factors were included in Nomogram.

RESULTS

396 patients were randomly assigned to the modelling group (n = 280) or the validation group (n = 116). Age, tissue-type, tumor size, AJCC stage, regional expansion and surgery were identified as independent prognostic factors (p < 0.05) which further combined to construct a nomogram. The C-index of internal validation for overall survival(OS) was 0.757, while the C-index of external validation for overall survival(OS) was 0.832. Both internal and external calibration curves show a good agreement between nomogram prediction and actual survival.

CONCLUSION

In this study, we established age, tumour size, AJCC stage, tissue type, surgery and tumor extension as independent prognostic factors for high-grade chondrosarcoma and constructed a nomogram to predict 3- and 5-year survival rates for high-grade chondrosarcoma.

A Simple-to-Use Nomogram for Predicting Postoperative Early Death Risk in Elderly Patients with Spinal Tumors: A Population-Based Study

Background

For elderly patients with primary spinal tumors, surgery is the best option for many elderly patients, in addition to palliative care. However, due to the unique physical function of elderly patients, the short-term prognosis is often unpredictable. It is therefore essential to develop a novel nomogram as a clinical aid to predict the risk of early death for elderly patients with primary spinal tumors who undergo surgery.

Materials and Methods

In this study, clinical data were obtained from 651 patients through the SEER database, and they were retrospectively analyzed. Logistic regression analyses were used for risk-factor screening. Predictive modeling was performed through the R language. The prediction models were calibrated as well as evaluated for accuracy in the validation cohort. The receiver operating characteristic (ROC) curve and the decision curve analysis (DCA) were used to evaluate the functionality of the nomogram.

Results

We identified four separate risk factors for constructing nomograms. The area under the receiver operating characteristic curve (training set 0.815, validation set 0.815) shows that the nomogram has good discrimination ability. The decision curve analysis demonstrates the clinical use of this nomogram. The calibration curve indicates that this nomogram has high accuracy. At the same time, we have also developed a web version of the online nomogram for clinical practitioners to apply.

Conclusions

We have successfully developed a nomogram that can accurately predict the risk of early death of elderly patients with primary spinal tumors undergoing surgery, which can provide a reference for clinicians.

Clinical characteristics, prognostic factors, and predictive model for elderly primary spinal tumor patients who are difficult to tolerate surgery or refuse surgery

Background

As a rare tumor, surgery is the best treatment for primary spinal tumors. However, for elderly patients who cannot undergo surgery, the prognosis is often difficult to evaluate. The purpose of this study was to identify the risk factors that may lead to death and predict the prognosis of elderly patients with primary spinal tumors who have not undergone surgical treatment.

Methods

In this study, 426 patients aged 60 years or older diagnosed with a primary spinal tumor between 1975 and 2015 were selected and included from the Surveillance, Epidemiology, and End Results database. A retrospective analysis was performed by using the Cox regression algorithm to identify independent prognostic factors. A nomogram model was developed based on the results. Multiple evaluation methods (calibration curve, receiver operating characteristic curve, and decision curve analyses) were used to evaluate and validate the performance of the nomogram.

Results

A nomogram was developed, with age, histological type, and stage as independent prognostic factors. The results indicated that the prognostic risk tended to increase significantly with age and tumor spread. Osteosarcoma was found to have the most prominent risk prognosis in this patient group, followed by chondrosarcoma and chordoma. The area under the curve and the C-index of the model were both close to or greater than 0.7, which proved the high-differentiation ability of the model. The calibration curve and decision curve analyses showed that the model had high predictive accuracy and application value.

Conclusions

We successfully established a practical nomogram to assess the prognosis of elderly patients with primary spinal tumors who have not undergone surgical treatment, providing a scientific basis for clinical management.

Individualized assessment of risk and overall survival in patients newly diagnosed with primary osseous spinal neoplasms with synchronous distant metastasis

Background

The prognosis of patients with primary osseous spinal neoplasms (POSNs) presented with distant metastases (DMs) is still poor. This study aimed to evaluate the independent risk and prognostic factors in this population and then develop two web-based models to predict the probability of DM in patients with POSNs and the overall survival (OS) rate of patients with DM.

Methods

The data of patients with POSNs diagnosed between 2004 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistics regression analyses were used to study the risk factors of DM. Based on independent DM-related variables, we developed a diagnostic nomogram to estimate the risk of DM in patients with POSNs. Among all patients with POSNs, those who had synchronous DM were included in the prognostic cohort for investigating the prognostic factors by using Cox regression analysis, and then a nomogram incorporating predictors was developed to predict the OS of patients with POSNs with DM. Kaplan-Meier (K-M) survival analysis was conducted to study the survival difference. In addition, validation of these nomograms were performed by using receiver operating characteristic (ROC) curves, the area under curves (AUCs), calibration curves, and decision curve analysis (DCA).

Results

A total of 1345 patients with POSNs were included in the study, of which 238 cases (17.70%) had synchronous DM at the initial diagnosis. K-M survival analysis and multivariate Cox regression analysis showed that patients with DM had poorer prognosis. Grade, T stage, N stage, and histological type were found to be significantly associated with DM in patients with POSNs. Age, surgery, and histological type were identified as independent prognostic factors of patients with POSNs with DM. Subsequently, two nomograms and their online versions (https://yxyx.shinyapps.io/RiskofDMin/ and https://yxyx.shinyapps.io/SurvivalPOSNs/) were developed. The results of ROC curves, calibration curves, DCA, and K-M survival analysis together showed the excellent predictive accuracy and clinical utility of these newly proposed nomograms.

Conclusion

We developed two well-validated nomograms to accurately quantify the probability of DM in patients with POSNs and predict the OS rate in patients with DM, which were expected to be useful tools to facilitate individualized clinical management of these patients.

Dynamic Predictive Models With Visualized Machine Learning for Assessing Chondrosarcoma Overall Survival

Chondrosarcoma is a malignant bone tumor with a low incidence rate. Accurate risk evaluation is crucial for chondrosarcoma treatment. Due to the limited reliability of existing predictive models, we intended to develop a credible predictor for clinical chondrosarcoma based on the Surveillance, Epidemiology, and End Results data and four Chinese medical institutes. Three algorithms (Best Subset Regression, Univariate and Cox regression, and Least Absolute Shrinkage and Selector Operator) were used for the joint training. A nomogram predictor including eight variables—age, sex, grade, T, N, M, surgery, and chemotherapy—is constructed. The predictor provides good performance in discrimination and calibration, with area under the curve ≥0.8 in the receiver operating characteristic curves of both internal and external validations. The predictor especially had very good clinical utility in terms of net benefit to patients at the 3- and 5-year points in both North America and China. A convenient web calculator based on the prediction model is available at https://drwenle029.shinyapps.io/CHSSapp, which is free and open to all clinicians.

Pathological Clinical Analysis and Imaging Manifestations for Spinal Bone Tumors Based on Cement Injection

In order to explore the imaging manifestations and pathological characteristics of spine tumors, this article explores the clinical diagnosis and treatment methods through multi-sample case analysis with the support of imaging, and proposes a targeted treatment method that uses a special PVP needle with a beveled puncture surface for puncture. Moreover, this article uses the supporting PVP syringe for bone cement injection, develops a health status questionnaire, and adopts a scoring method for comprehensive assessment. The purpose of this article is to show that through the combination of preoperative radiotherapy and postoperative bracing, bone cement injection to treat vertebral tumors can immediately obtain satisfactory pain relief. Finally, through case analysis and image performance, we can see that the method proposed in this article has a certain effect.

Brain Metastasis in Patients with Small Cell Lung Cancer

Purpose

To characterize the risk factors for brain metastasis (BM) at presentation and analyze the prognostic factors for patients with small cell lung cancer (SCLC).

Patients and Methods

Patients were recruited from the SEER database between 2010 and 2016. They were divided into two groups according to BM status. The incidence trends of SCLC and its BM were analyzed by joinpoint software. The risk factors for BM in SCLC were identified by binary logistic regression models. The prognostic factors for SCLC patients with BM were identified by Cox proportional hazard models.

Results

The incidence of SCLC and its BM significantly decreased after 2010. Totally 11,093 patients were collected, including 1717 (15.5%) patients with BM and 9376 (84.5%) patients without BM. In multivariate logistic regression analysis, age, male and higher T stage were independent risk factors for BM in SCLC patients at presentation. SCLC patients with BM showed inferior survival to those without BM. In multivariate Cox regression analysis, increasing age, large tumor size, and higher N stage were risk factors for poor prognosis, while other race, surgery, adjuvant radiotherapy, and chemotherapy were protective factors for SCLC patients with BM. A nomogram was developed for prognosis evaluation of such patients.

Conclusion

Age, male and higher T stage were risk factors for BM in SCLC patients at presentation. Increasing age, large tumor size, and advanced N stage may predict poor survival for SCLC patients with BM. Multidisciplinary therapies may provide clinical benefits. This study will help identify patients with higher BM risk and hopefully improve their clinical outcome.

Primary osseous chondrosarcoma in the lumbar spine: case report and literature review with analysis

Primary spinal chondrosarcoma (CS) is rare. Only a few previous case reports have included a detailed description of the surgical process used to treat the CS. In addition, a paucity of documentation exists comparing differences in the outcomes between the approaches in en bloc resection. Here, we present a case of CS in the lumbar (L) spine treated with two-stage (anterior and posterior approach) en bloc surgery and analyze the differences between one-stage and two-stage approaches in the treatment of primary lumbar CS. A 30-year-old male patient with an L3 vertebral body CS presented with back pain and lower limb weakness. Lumbar spine magnetic resonance imaging (MRI) showed an L3 vertebral body tumor with cord and root compression. Two-stage surgery comprising posterior total laminectomy and transpedicular screw fixation over L2–L4 in the first stage, with subsequent anterior corpectomy, cage implantation, and anterior lumbar interbody fusion was performed to achieve total tumor removal and stabilization. The patient’s symptoms improved postoperatively, with no recurrence as of the 2-year follow-up. The analysis of previous similar cases showed that two-stage surgery, compared with one-stage surgery, appears to be beneficial in lumbar spine multisegment disease, providing a lower recurrence rate.

A Competing Risk-based Prognostic Model to Predict Cancer-specific Death of Patients with Spinal and Pelvic Chondrosarcoma

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

The aim of this study was to develop and validate a competing-risk-based prognostic model and a nomogram for predicting the three- and five-year probability of cancer-specific death (CSD) in patients with spinal and pelvic chondrosarcoma.

SUMMARY OF BACKGROUND DATA

The issue of competing risk has rarely been addressed and discussed in survival analysis of bone sarcoma. In addition, the Fine and Gray model, a more accurate method for survival analysis in the context of competing risk, has also been less reported in prognostic study of chondrosarcoma.

METHODS

A total of 623 patients with spinal or pelvic chondrosarcoma were identified from the SEER database and were divided into a training and a validation cohort. These two cohorts were used to develop and validate a prognostic model to predict the 3- and 5-year probability of CSD, considering non-CSD as competing risk. The C-index, calibration plot, and decision curve analysis were used to assess the predictive performance and clinical utility of the model.

RESULTS

Older age (subdistribution hazards ratio [SHR]: 1.02, 95% confidence interval [CI]: 1.01∼1.03; P = 0.013), high grade (SHR: 2.68, 95% CI: 1.80∼3.99; P < 0.001), regional involvement (SHR: 1.66, 95% CI: 1.06∼2.58; P = 0.026), distant metastasis (SHR: 5.18, 95% CI: 3.11∼8.62; P < 0.001) and radical resection (SHR: 0.38, 95% CI: 0.24∼0.60; P < 0.001) were significantly associated with the incidence of CSD. These factors were used to build a competing-risk-based model and a nomogram to predict CSD. The C-index, calibration plot, and decision curve analysis indicated that the nomogram performs well in predicting CSD and is suitable for clinical use.

CONCLUSION

A competing-risk based prognostic model is developed to predict the probability of CSD of patients with spinal and pelvic chondrosarcoma. This nomogram performs well and is suitable for clinical use.Level of Evidence: 4.

Identifying the Risk Factors and Estimating the Prognosis in Patients with Pelvis and Spine Ewing Sarcoma: A Population-Based Study

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

The study was designed to: (1) figure out risk factors of metastasis; (2) explore prognostic factors and develop a nomogram for pelvis and spine Ewing sarcoma (PSES).

SUMMARY OF BACKGROUND DATA

Tools to predict survival of PSES are still insufficient. Nomogram has been widely developed in clinical oncology. Moreover, risk factors of PSES metastasis are still unclear.

METHODS

The data were collected and analyzed from the Surveillance, Epidemiology, and End Results (SEER) database. The optimal cutoff values of continuous variables were identified by X-tile software. The prognostic factors of survival were performed by Kaplan-Meier method and multivariate Cox proportional hazards modeling. Nomograms were further constructed for estimating 3- and 5-year cancer-specific survival (CSS) and overall survival (OS) by using R with rms package. Meanwhile, Pearson χ2 test or Fisher exact test, and logistic regression analysis were used to analyze the risk factors for the metastasis of PSES.

RESULTS

A total of 371 patients were included in this study. The 3- and 5-year CSS and OS rate were 65.8 ± 2.6%, 55.2 ± 2.9% and 64.3 ± 2.6%, 54.1 ± 2.8%, respectively. The year of diagnosis, tumor size, and lymph node invasion were associated with metastasis of patients with PSES. A nomogram was developed based on identified factors including: age, tumor extent, tumor size, and primary site surgery. The concordance index (C-index) of CSS and OS were 0.680 and 0.679, respectively. The calibration plot showed the similar trend of 3-year, 5-year CSS, and OS of PSES patients between nomogram-based prediction and actual observation, respectively.

CONCLUSION

PSES patients with earlier diagnostic year (before 2010), larger tumor size (>59 mm), and lymph node invasion, are more likely to have metastasis. We developed a nomogram based on age, tumor extent, tumor size, and surgical treatments for determining the prognosis for patients with PSES, while more external patient cohorts are warranted for validation.Level of Evidence: 3.

Risk analysis of pulmonary metastasis of chondrosarcoma by establishing and validating a new clinical prediction model: a clinical study based on SEER database

Background

The prognosis of lung metastasis (LM) in patients with chondrosarcoma was poor. The aim of this study was to construct a prognostic nomogram to predict the risk of LM, which was imperative and helpful for clinical diagnosis and treatment.

Methods

Data of all chondrosarcoma patients diagnosed between 2010 and 2016 was queried from the Surveillance, Epidemiology, and End Results (SEER) database. In this retrospective study, a total of 944 patients were enrolled and randomly splitting into training sets (n = 644) and validation cohorts(n = 280) at a ratio of 7:3. Univariate and multivariable logistic regression analyses were performed to identify the prognostic nomogram. The predictive ability of the nomogram model was assessed by calibration plots and receiver operating characteristics (ROCs) curve, while decision curve analysis (DCA) and clinical impact curve (CIC) were applied to measure predictive accuracy and clinical practice. Moreover, the nomogram was validated by the internal cohort.

Results

Five independent risk factors including age, sex, marital, tumor size, and lymph node involvement were identified by univariate and multivariable logistic regression. Calibration plots indicated great discrimination power of nomogram, while DCA and CIC presented that the nomogram had great clinical utility. In addition, receiver operating characteristics (ROCs) curve provided a predictive ability in the training sets (AUC = 0.789, 95% confidence interval [CI] 0.789–0.808) and the validation cohorts (AUC = 0.796, 95% confidence interval [CI] 0.744–0.841).

Conclusion

In our study, the nomogram accurately predicted risk factors of LM in patients with chondrosarcoma, which may guide surgeons and oncologists to optimize individual treatment and make a better clinical decisions.

Trial registration

JOSR-D-20-02045, 29 Dec 2020.

Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study

Introduction

We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas.

Methods

The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes (C-indexes) and calibration plots.

Results

All 813 patients were randomly divided into a training group (n = 572) and a validation group (n = 241). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C-indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups.

Conclusions

The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.

Development and validation of a novel prognostic model for long-term overall survival in liposarcoma patients: a population-based study

Objective

To construct and validate a clinically accurate and histology-specific nomogram to predict overall survival (OS) among liposarcoma (LPS) patients.

Methods

We retrospectively screened eligible patients with LPS diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database. We screened independent predictors for the nomogram using univariate and multivariate analyses. We then evaluated the prognostic accuracy of the nomogram by receiver operating characteristic (ROC) curve analysis and Harrell’s concordance index. The prognostic performances of the nomogram and the American Joint Committee on Cancer (AJCC) seventh edition staging system were compared using integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analyses (DCA).

Results

A novel nomogram was developed using independent prognostic variables, which exhibited excellent predictive performances for 3- and 5-year OS according to ROC curves. The C-index proved that the proposed nomogram had better prognostic accuracy for LPS than the traditional AJCC system, while the NRI, IDI, and DCA of the nomogram indicated better clinical net benefit.

Conclusions

The proposed nomogram can predict 3- and 5-year OS of LPS patients with reliable accuracy and may thus help clinicians to develop appropriate clinical therapies and counseling strategies to prolong the life expectancy of these patients.

Development and validation of nomograms predicting cancer-specific survival of vulvar cancer patients: based on the Surveillance, Epidemiology, and End Results Program

OBJECTIVE

To explore potential prognostic factors and develop nomograms to predict the cancer-specific survival of patients with vulvar squamous cell carcinoma (SCC) and patients with vulvar melanoma.

METHODS

Cases of vulvar SCC and melanoma were retrieved from the Surveillance, Epidemiology, and End Results (SEER) Program, and randomly segregated into training and test sets. Based on the training set, univariate and multivariate Cox proportional hazard regressions evaluate the association between key demographic/clinical characteristics and vulvar cancer survival. Potential prognostic factors were included to construct nomograms for the prediction of 3-year and 5-year survival probabilities.

RESULTS

Age, tumor size, stage, surgery, and chemotherapy were potential factors associated with vulvar cancer survival. The C-indices for the training and test sets were 0.82 and 0.81 for SCC, and 0.73 and 0.70 for melanoma. Calibration curves revealed correlated agreements between nomogram-based probability and actual survival status.

CONCLUSION

Nomograms were developed to predict cancer-specific survival of patients with vulvar cancer, accordingly identifying the subgroup at high risk of cancer-specific mortality.

Prognostic Nomograms to Predict Overall Survival and Cancer-specific Survival in Sacrum/Pelvic Chondrosarcoma (SC) Patients: A Population-based Propensity Score-matched Study

STUDY DESIGN

A longitudinal cohort study.

OBJECTIVE

The objective of this study was to evaluate the prognostic factors and determine the difference between different surgery scopes. Nomograms were constructed and validated to predict overall survival (OS) and cancer-specific survival (CSS) of sacrum/pelvic chondrosarcoma (SC) patients.

SUMMARY OF BACKGROUND DATA

Chondrosarcoma is a bone malignancy which is reported to be resistant to both chemotherapy and radiotherapy. Therefore, surgery is the most preferred treatment method. However, this remains a great challenge due to the complex anatomy of the area.

MATERIALS AND METHODS

Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with conventional SC between 1998 and 2016 was retrieved for analysis. Cox analysis was used to estimate the mortality hazards ratios among patients. Propensity score matching was used to compare different surgery scope. Nomograms were constructed to predict the OS and CSS of patients with SC.

RESULTS

A total of 377 patients were included in this study. The cutoff value for tumor size was considered to be 118 mm. The concordance indices (C-index) value for nomogram predictions of CSS were 0.871. Following propensity score matching, 158 patients were selected for the second time and its result showed no significant difference between the scope of surgery.

CONCLUSIONS

Tumor size was considered to be closely related to the outcome of SC. There is no significant difference in the scope of surgery and limb salvage can be considered. The nomograms can precisely predict OS and CSS in patients with SC. These could help clinicians to perform survival assessments and identify patients at high risk.

LEVEL OF EVIDENCE

Level IV.

Primary Tumor Resection Prolongs Survival in Spinal Chondrosarcoma Patients With Distant Metastasis

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

To investigate (1) whether resection of primary tumor improves survival of metastatic spinal chondrosarcoma patients and (2) which subgroups of metastatic spinal chondrosarcoma patients benefit more from primary tumor resection.

SUMMARY OF BACKGROUND DATA

Surgical resection is the mainstay of treatment for spinal chondrosarcoma, as chondrosarcoma is inherently resistant to radiotherapy and chemotherapy. However, evidence which justifies resection of the primary tumor for patients with metastatic spinal chondrosarcoma is still lacking.

METHODS

We retrospectively included 110 patients with metastatic spinal chondrosarcoma in the Surveillance, Epidemiology, and End Results database from 1983 to 2016. The association between primary tumor resection and survival was evaluated using Kaplan-Meier analyses, log-rank tests, and multivariable Cox analyses. The effect of primary tumor resection on survival was further assessed in subgroups stratified by histologic subtype, tumor grade, and age.

RESULTS

Overall, 110 patients were divided into surgery group (n = 55, 50%) and nonsurgery group (n = 55, 50%). Primary tumor resection was associated with both prolonged overall survival (hazard ratio 0.262, 95% confidence interval 0.149-0.462, P < 0.001) and cancer-specific survival (hazard ratio 0.228, 95% confidence interval 0.127-0.409, P < 0.001). When we focused on surgical effects in subgroups, primary tumor resection conferred survival advantage on patients with conventional subtype, grade I to III malignancy, and an age younger than 70 years old (P < 0.001 for overall and cancer-specific survival). However, primary tumor resection brought limited survival benefit for patients with dedifferentiated subtype and patients over 70 years old.

CONCLUSION

The present population-based study for the first time reports a clear association between primary tumor resection and prolonged survival in metastatic spinal chondrosarcoma patients. Specifically, primary tumor resection was associated with improved survival in patients with conventional subtype, grade I to III malignancy, and an age younger than 70 years old.

LEVEL OF EVIDENCE

4.

Nomograms for predicting overall survival and cancer-specific survival of chondroblastic osteosarcoma patients

BACKGROUND

The establishment of precise and personalized prediction systems for chondroblastic osteosarcoma patients is important for guiding the treatment.

METHODS

The univariate logrank test and multivariate Cox regression analysis were performed to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Nomograms were constructed to estimate the OS and CSS based on these factors. Internal and external validation was performed. The predictive power of the nomograms was determined by C-index and calibration plots.

RESULTS

A total of 401 chondroblastic osteosarcoma cases were identified. Univariate and multivariate analysis revealed that age at diagnosis, histological grade, tumor size, Surveillance, Epidemiology, and End Results stage, and surgical resection were independent prognostic factors for OS and CSS. The five factors were incorporated to construct the nomograms for estimating the 3- and 5-year OS and CSS. The C-index values for the internal validation of the OS and CSS nomogram were 0.732 and 0.746, respectively, and for the external validation were 0.780 and 0.808, respectively. The calibration curves revealed that the predicted OS and CSS could well match the actual survival rate.

CONCLUSIONS

The nomograms for predicting 3- and 5-year OS and CSS were constructed and were proved to be accurate and reliable by the internal and external validation.

Conditional Survival Analysis for Spinal Chondrosarcoma Patients After Surgical Resection

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

To evaluate conditional survival after surgical resection for spinal chondrosarcoma patients.

SUMMARY OF BACKGROUND DATA

Survival estimates are usually reported as survival from the time of surgery, but survival probabilities can change over time. Conditional survival, which is a measure of prognosis for patients who have survived a defined period of time, may be more clinically precise and relevant. However, data on conditional survival for spinal chondrosarcoma patients after surgical resection are still lacking.

METHODS

We used the Surveillance, Epidemiology, and End Results (SEER) database to identify 436 spinal chondrosarcoma patients who underwent surgical resection from 1994 and 2013. Kaplan-Meier analyses and Cox regression modeling were performed to evaluate prognostic factors associated with overall survival. Five-year conditional survival (i.e., probability of surviving an additional 5 years, given that a patient has already survived x years) was calculated as 5-CS(x) = OS(x+5)/OS(x). The effect of prognostic factors on conditional survival was also explored.

RESULTS

Four hundred thirty six patients were included in the study cohort. Overall, 1-, 3-, and 5-year overall survival were 92.8%, 79.1%, and 70.3%, respectively. Five-year conditional survival at 1, 3, and 5 years after surgery were 72.9%, 79.0%, and 87.5%. The overall survival rates were lower in cases of age more than or equal to 60 years, male patient, dedifferentiated subtype, Grade III tumor, tumor size more than or equal to 10 cm, distant metastasis, and radiotherapy. Conditional survival improved over time in each subgroup divided by age, sex, race, year of diagnosis, grade, tumor size, extent of disease (EOD), and radiotherapy. In addition, patients with the least favorable prognosis at baseline experienced the greatest increase in 5-year conditional survival over time (e.g., Grade I/II: 78.0%-89.7%, Δ11.7% vs. Grade III: 36.5%-66.6%, Δ30.1%; Localized/Regional: 72.9%-88.1%, Δ15.2% vs. Distant: 43.5%-74.1%, Δ30.6%).

CONCLUSION

Conditional survival for spinal chondrosarcoma patients after surgical resection improves over time, especially for patients with initial high-risk characteristics. Information derived from conditional survival analysis may provide individualized approaches to surveillance and treatment of spinal chondrosarcoma.

LEVEL OF EVIDENCE

4.

Does Radiotherapy with Surgery Improve Survival and Decrease Progression to Multiple Myeloma in Patients with Solitary Plasmacytoma of Bone of the Spine?

OBJECTIVE

To investigate the outcomes of radiotherapy with or without surgery during treatment of patients with solitary plasmacytoma of bone (SBP) of the spine.

METHODS

Patients diagnosed with SBP of the spine treated with radiotherapy with or without surgery were identified and extracted from the SEER database. Propensity score matched (PSM) analysis was performed to balance patient characteristics between radiotherapy alone and radiotherapy with surgery groups. Patients in different age-groups were stratified and analyzed.

RESULTS

A total of 1275 patients with SBP of the spine treated with radiotherapy with or without surgery were extracted from the SEER database. Before PSM, the unadjusted Kaplan-Meier curve showed that the radiotherapy with surgery group had worse overall survival than did the radiotherapy without surgery group (both P < 0.05), whereas the difference of overall survival was attenuated after PSM. Stratified analysis found that the radiotherapy with surgery group had less progression to multiple myeloma for young patients (age <45 years) with SBP of the spine than did the radiotherapy without surgery group.

CONCLUSIONS

The results of our study suggest that radiotherapy with surgery may show less progression to multiple myeloma for younger patients with SBP of the spine.

Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non-Metastatic Chondrosarcoma from the SEER Database

OBJECTIVE

To identify prognostic factors and establish nomograms for predicting overall survival (OS) and cause specific survival (CSS) of patients with non-metastatic chondrosarcoma.

METHODS

We collected information on patients with non-metastatic chondrosarcoma from the Surveillance, Epidemiology, and End Results (SEER) database between 2005 and 2014, together with data from the First Affiliated Hospital of Zhengzhou University from 2011 to 2016. Variables including patients' baseline demographics (age, race, and gender), tumor characteristics (tumor size and extension, histology subtype, primary site, and American Joint Committee on Cancer [AJCC] stage), therapy (surgery, chemotherapy, and radiotherapy), and socioeconomic status (SES) were extracted for further analysis. OS and CSS were retrieved as our researching endpoints. Patients from the database were regarded as the training set, and univariate analysis, Lasso regression and multivariate analysis as well as the random forest were used to explore the predictors and establish nomograms. To validate nomograms internally and externally, we applied bootstrapped validation internally with the training dataset, while the dataset for external validation was obtained from the First Affiliated Hospital of Zhengzhou University. We estimated the discriminative ability of nomograms based on Cox proportional hazard regression models by means of calibration curves and the concordance index (C-index) of internal and external validation.

RESULTS

After the implementation of exclusion criteria, there were 1267 patients in the training set and 72 patients in the testing set with non-metastatic chondrosarcomas. Age, gender, grade, histological subtype, primary site, surgery, radiation, chemotherapy, being employed/unemployed, tumor size, and tumor extension were significantly associated with prognosis in the univariate analysis. Age, gender, tumor size and extension, primary site, surgery, radiotherapy, chemotherapy, histological grade, and subtype were independent prognostic factors in the Cox models. The C-index of nomograms (internal: OS, 0.787; CSS, 0.821; external: OS, 0.777; CSS, 0.821) were higher than following conventional systems: AJCC sixth (OS, 0.640; CSS, 0.673) and seventh edition (OS, 0.675; CSS, 0.711).

CONCLUSIONS

Age, gender, tumor size and extension, surgery, histological grade, and subtype were independent prognostic factors for both OS and CSS. In addition, we revealed that chondrosarcomas in the trunk, radiotherapy, and chemotherapy were correlated with poor prognosis. Our nomograms based on significant clinicopathologic features can well predict the 3-year and 5-year survival probability of patients with non-metastatic chondrosarcoma and assist oncologists in making accurate survival evaluation.

Clinical features and prognostic analysis of patients with chest wall chondrosarcoma

Chest wall chondrosarcoma is a rare malignant tumor of the bone. This study is aimed to identify the prognostic determinants of chest wall chondrosarcoma. We used the Surveillance, Epidemiology, and End Results (SEER) database to identify patients with chest wall chondrosarcoma from 1973 to 2015. Statistical analyses were performed using Kaplan-Meier method and Cox regression proportional hazards. A total of 779 patients were identified from the SEER database. The overall survival (OS) and cancer-specific survival (CSS) rates of the entire group at 10 years were 66.2% and 77.2%, respectively. On multivariate Cox regression, age ≤40 years, localized tumor stage, low tumor grade, surgery, and no radiotherapy were significantly associated with improved both OS and CSS. This study may help clinicians to predict survival of patients with chest wall chondrosarcoma and to provide appropriate treatment recommendations.

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

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.

Nomograms Predict Cancer-Specific and Overall Survival of Patients With Primary Limb Leiomyosarcoma

To date, there have been no data to predict the survival of patients with leiomyosarcoma from soft limb tissue because of the rarity of this disease. Nomograms have been widely applied in clinical oncology to precisely predict the survival of individual patients. This was a retrospective study to construct and validate nomograms to predict the cancer-specific survival (CSS) and overall survival (OS) of patients with primary limb leiomyosarcoma (PL-LMS). A total of 1,208 patients with LMS from limb soft tissue were collected from the Surveillance, Epidemiology, and End Results database from 1975 to 2015. We identified independent prognostic factors using univariate and multivariate Cox analyses. These prognostic factors were then included in the nomograms to predict 3- and 5-year CSS and OS rates. Finally, we validated the nomograms internally and externally. A total of 1208 patients were collected and divided into validation (N = 604) and training (N = 604) groups. Age, race, grade, tumor size, stage, and surgical types were demonstrated as independent prognostic factors for CSS and OS (all p < 0.05) and further used to construct the nomograms. The concordance index (C-index) for CSS was 0.857 for internal validation and 0.727 for external validation. The C-index for OS and CSS both demonstrated that the nomogram prediction agreed perfectly with actual survival. We developed nomograms to predict CSS and OS in PL-LMS patients and can benefit from using them to identify patients' mortality risk and make more precise assessments regarding survival. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1649-1657, 2019.

Development and validation of a nomogram containing the prognostic determinants of chondrosarcoma based on the Surveillance, Epidemiology, and End Results database

BACKGROUND

We aimed to develop and validate a reliable nomogram for predicting the disease-specific survival (DSS) of chondrosarcoma patients.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was queried from 2004 to 2015 to identify cases of histologically confirmed chondrosarcoma. Multivariate Cox regression analysis was performed to identify independent prognostic factors and construct a nomogram for predicting the 3- and 5-year DSS rates. Predictive values were compared between the new model and the American Joint Committee on Cancer (AJCC) staging system using concordance indexes (C-indexes), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

RESULTS

Multivariate Cox regression identified 1180 patients, who were used to establish a nomogram based on a new model containing the predictive variables of age, socioeconomic status, tumor size, surgery status, chemotherapy status, and AJCC staging. In the nomogram, age at diagnosis is the factor with the highest risk, followed by AJCC stage IV and tumor size > 100 mm. Both the C-index and the calibration plots demonstrated the good performance of the nomogram. Moreover, both NRI and IDI were improved compared to the AJCC staging system, and also DCA demonstrated that the nomogram is clinically useful.

CONCLUSION

We have developed a reliable nomogram for determining the prognosis and treatment outcomes of chondrosarcoma patients that is superior to the traditional AJCC staging system.

Survival analysis of patients with metastatic osteosarcoma: a Surveillance, Epidemiology, and End Results population-based study

PURPOSE

The present study is aimed at investigating whether (1) primary tumour surgery confers an improved survival on patients with metastatic osteosarcoma and (2) primary tumour surgery influences survival of patients with metastatic osteosarcoma differently according to primary tumour site.

METHODS

We retrospectively identified 517 patients with high-grade, metastatic osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) database between 1994 and 2013. The effect of primary tumour surgery on survival was assessed using Kaplan-Meier analyses, log-rank tests, and multivariate Cox proportional hazard regression modeling.

RESULTS

Of those 517 patients with metastatic osteosarcoma in the cohort, 351 patients (68%) underwent primary surgery, and 166 patients (32%) did not undergo surgery. Primary tumour surgery was associated with increased overall survival (hazard ratio (HR) = 0.457, 95% CI 0.354-0.590, p < 0.001) and cancer-specific survival (HR = 0.422, 95% CI 0.325-0.550, p < 0.001). When we focused on different primary tumour sites, receipt of primary tumour surgery significantly prolonged the survival of patients with extremity osteosarcoma (p < 0.05 for overall and cancer-specific survival). However, for patients with pelvis/spine osteosarcoma, both univariate and multivariate analyses indicated that primary tumour surgery might not be associated with improved survival (p > 0.05 for overall and cancer-specific survival).

CONCLUSIONS

Our study is the first population-based analysis to provide evidence of a favourable prognostic impact of primary tumour surgery on metastatic extremity osteosarcoma patients but not metastatic axial (pelvis/spine) osteosarcoma patients. Moreover, we found that surgery type (resection of the primary tumor without amputation vs. amputation) did not influence survival in patients with metastatic osteosarcoma.

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

Purpose

A prognostic nomogram was applied to predict survival in osteosarcoma patients.

Patients and methods

Data collected from 2,195 osteosarcoma patients in the Surveillance, Epidemiology, and End Results (SEER) database between 1983 and 2014 were analyzed. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These were incorporated into a nomogram to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates. Internal and external data were used for validation. Concordance indices (C-indices) were used to estimate nomogram accuracy.

Results

Patients were randomly assigned into a training cohort (n=1,098) or validation cohort (n=1,097). Age at diagnosis, tumor site, histology, tumor size, tumor stage, use of surgery, and tumor grade were identified as independent prognostic factors via univariate and multivariate Cox analyses (all P<0.05) and then included in the prognostic nomogram. C-indices for OS and CSS prediction in the training cohort were 0.763 (95% CI 0.761–0.764) and 0.764 (95% CI 0.762–0.765), respectively. C-indices for OS and CSS prediction in the external validation cohort were 0.739 (95% CI 0.737–0.740) and 0.740 (95% CI, 0.738–0.741), respectively. Calibration plots revealed excellent consistency between actual survival and nomogram prediction.

Conclusion

Nomograms were constructed to predict OS and CSS for osteosarcoma patients in the SEER database. They provide accurate and individualized survival prediction.