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

Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

BACKGROUND

Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.

METHODS

Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC).

RESULTS

The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUC: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians.

CONCLUSIONS

This study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

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.

Is the Number of National Database Research Studies in Musculoskeletal Sarcoma Increasing, and Are These Studies Reliable?

BACKGROUND

Large national databases have become a common source of information on patterns of cancer care in the United States, particularly for low-incidence diseases such as sarcoma. Although aggregating information from many hospitals can achieve statistical power, this may come at a cost when complex variables must be abstracted from the medical record. There is a current lack of understanding of the frequency of use of the Surveillance, Epidemiology, and End Results (SEER) database and the National Cancer Database (NCDB) over the last two decades in musculoskeletal sarcoma research and whether their use tends to produce papers with conflicting findings.

QUESTIONS/PURPOSES

(1) Is the number of published studies using the SEER and NCDB databases in musculoskeletal sarcoma research increasing over time? (2) What are the author, journal, and content characteristics of these studies? (3) Do studies using the SEER and the NCDB databases for similar diagnoses and study questions report concordant or discordant key findings? (4) Are the administrative data reported by our institution to the SEER and the NCDB databases concordant with the data in our longitudinally maintained, physician-run orthopaedic oncology dataset?

METHODS

To answer our first three questions, PubMed was searched from 2001 through 2020 for all studies using the SEER or the NCDB databases to evaluate sarcoma. Studies were excluded from the review if they did not use these databases or studied anatomic locations other than the extremities, nonretroperitoneal pelvis, trunk, chest wall, or spine. To answer our first question, the number of SEER and NCDB studies were counted by year. The publication rate over the 20-year span was assessed with simple linear regression modeling. The difference in the mean number of studies between 5-year intervals (2001-2005, 2006-2010, 2011-2015, 2016-2020) was also assessed with Student t-tests. To answer our second question, we recorded and summarized descriptive data regarding author, journal, and content for these studies. To answer our third question, we grouped all studies by diagnosis, and then identified studies that shared the same diagnosis and a similar major study question with at least one other study. We then categorized study questions (and their associated studies) as having concordant findings, discordant findings, or mixed findings. Proportions of studies with concordant, discordant, or mixed findings were compared. To answer our fourth question, a coding audit was performed assessing the concordance of nationally reported administrative data from our institution with data from our longitudinally maintained, physician-run orthopaedic oncology dataset in a series of patients during the past 3 years. Our orthopaedic oncology dataset is maintained on a weekly basis by the senior author who manually records data directly from the medical record and sarcoma tumor board consensus notes; this dataset served as the gold standard for data comparison. We compared date of birth, surgery date, margin status, tumor size, clinical stage, and adjuvant treatment.

RESULTS

The number of musculoskeletal sarcoma studies using the SEER and the NCDB databases has steadily increased over time in a linear regression model (β = 2.51; p < 0.001). The mean number of studies per year more than tripled during 2016-2020 compared with 2011-2015 (39 versus 13 studies; mean difference 26 ± 11; p = 0.03). Of the 299 studies in total, 56% (168 of 299) have been published since 2018. Nineteen institutions published more than five studies, and the most studies from one institution was 13. Orthopaedic surgeons authored 35% (104 of 299) of studies, and medical oncology journals published 44% (130 of 299). Of the 94 studies (31% of total [94 of 299]) that shared a major study question with at least one other study, 35% (33 of 94) reported discordant key findings, 29% (27 of 94) reported mixed key findings, and 44% (41 of 94) reported concordant key findings. Both concordant and discordant groups included papers on prognostic factors, demographic factors, and treatment strategies. When we compared nationally reported administrative data from our institution with our orthopaedic oncology dataset, we found clinically important discrepancies in adjuvant treatment (19% [15 of 77]), tumor size (21% [16 of 77]), surgery date (23% [18 of 77]), surgical margins (38% [29 of 77]), and clinical stage (77% [59 of 77]).

CONCLUSION

Appropriate use of databases in musculoskeletal cancer research is essential to promote clear interpretation of findings, as almost two-thirds of studies we evaluated that asked similar study questions produced discordant or mixed key findings. Readers should be mindful of the differences in what each database seeks to convey because asking the same questions of different databases may result in different answers depending on what information each database captures. Likewise, differences in how studies determine which patients to include or exclude, how they handle missing data, and what they choose to emphasize may result in different messages getting drawn from large-database studies. Still, given the rarity and heterogeneity of sarcomas, these databases remain particularly useful in musculoskeletal cancer research for nationwide incidence estimations, risk factor/prognostic factor assessment, patient demographic and hospital-level variable assessment, patterns of care over time, and hypothesis generation for future prospective studies.

LEVEL OF EVIDENCE

Level III, therapeutic study.

The Epidemiology of Chondrosarcoma in Iran Based on Iran National Cancer Registry

Background: Chondrosarcoma is regarded as the second most common primary bone malignancy following osteosarcoma. Objectives: The present study aimed at determining the epidemiology, incidence, and survival rate of chondrosarcoma in the Iranian population, according to the Iran National Cancer Registry (INCR). Methods: In an epidemiological study, patients with limb chondrosarcoma were evaluated based on INCR data between 2008 and 2015. Data included patients’ demographic characteristics, date of diagnosis, location of the tumor, patient’s survival, and type of tumor based on the International Classification of Diseases for Oncology (ICD-O-3; first revision, third edition) were collected and analyzed. Results: Out of 732 enrolled patients, 425 patients (58.06%) were male and 307 (41.94%) were female with a mean age of 44.08 (SD = 19.31) and 45.06 (SD = 18.72), respectively. Age-standardized incidence rates (ASIR) were 1.73 and 1.27 per 1 million person-years for males and females, respectively. Conventional chondrosarcoma was the most common subtype with ASIR 1.28 and constituted 84.7% of patients with chondrosarcoma. About 71.03% of all Chondrosarcoma patients (70.35% of males and 71.99% of females) were between 20 to 59 years old. The 1-, 3-, 5-, and 7-year survival rates of patients were 0.87, 0.73, 0.57, and 0.47, respectively. Also, the mean survival time was 6.12 years (95% CI: 5.85 - 7.39). Conclusions: The incidence of chondrosarcoma in Iran is not as high as in other countries, but as patients are younger in Iran, the survival rate is worse compared to other countries. Therefore, better case findings and better care are needed to improve the patients' outcomes in Iran.

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.

A novel nomogram and risk classification system predicting the Ewing sarcoma: a population-based study

Ewing sarcoma (ES) is a rare disease that lacks a prognostic prediction model. This study aims to develop a nomogram and risk classification system for estimating the probability of overall survival (OS) of patients with ES. The clinicopathological data of ES were collected from the Surveillance, Epidemiology and Final Results (SEER) database from 2010 to 2018. The primary cohort was randomly assigned to the training set and the validation set. Univariate and multiple Cox proportional hazard analyses based on the training set were performed to identify independent prognostic factors. A nomogram was established to generate individualized predictions of 3- and 5-year OS and evaluated by the concordance index (C-index), the receiver operating characteristic curve (ROC), the calibration curve, the integrated discrimination improvement (IDI) and the net reclassification improvement (NRI). Based on the scores calculated with the nomogram, ES patients were divided into three risk groups to predict their survival. A total of 935 patients were identified, and a nomogram consisting of 6 variables was established. The model provided better C-indices of OS (0.788). The validity of the Cox model assumptions was evaluated through the Schönfeld test and deviance residual. The ROC, calibration curve, IDI and NRI indicated that the nomogram exhibited good performance. A risk classification system was built to classify the risk group of ES patients. The nomogram compares favourably and accurately to the traditional SEER tumour staging systems, and risk stratification provides a more convenient and effective tool for clinicians to optimize treatment options.

Establishment and Validation of Prognostic Nomograms for Patients With Parotid Gland Adenocarcinoma Not Otherwise Specified: A SEER Analysis From 2004 to 2016

Background: Parotid gland adenocarcinoma not otherwise specified (PANOS) is a rare malignant tumor with limited data on its characteristics and prognosis. This research is aimed at characterizing PANOS and developing prognostic prediction models for patients with PANOS. Methods: Cases from 2004-2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) Program database. Univariate and multivariate Cox regression were applied to ascertain the factors associated with survival. Competing risk analysis and Gray's tests were employed to analyze cancer-specific death. Propensity score matching (1:1) was conducted to reduce the influence of confounding variables. Results: A total of 446 patients with a median age of 66 years were selected, of which 307 were diagnosed with stage III/IV PANOS. The 5-year overall survival (OS) rate of all patients was 51.8%, and the median survival time was 66 months. Surgical treatment clearly improved survival time (p < 0.001). In the subgroup analysis, radiotherapy showed survival benefits in patients with stage III/IV disease (p < 0.001). Multivariate Cox regression analyses showed that age, T classification, N classification, M classification and surgery were independent prognostic indicators for OS; T classification, N classification, M classification and surgery were independent risk factors for cancer-specific survival (CSS). In addition, age was independently associated with other cause-specific death. Based on the results of multivariate analysis, two nomograms were developed and verified by the concordance index (C-index) (0.747 and 0.780 for OS and CSS) and the area under the time-dependent receiver operating characteristic (ROC) curve (0.756, 0.764, and 0.819 regarding for nomograms predicting 3-, 5-, and 10- year OS, respectively and 0.794, 0.789, and 0.806 for CSS, respectively). Conclusions: Our study clearly presents the clinicopathological features and survival analysis of patients with PANOS. In addition, our constructed nomogram prediction models may assist physicians in evaluating the individualized prognosis and deciding on treatment for patients.

Nomograms for Predicting Cancer-Specific Survival of Patients with Gingiva Squamous Cell Carcinoma: A Population-Based Study

OBJECTIVE

The use of the traditional American Joint Committee on Cancer (AJCC) staging system alone has limitations in predicting the survival of gingiva squamous cell carcinoma (GSCC) patients. We aimed to establish a comprehensive prognostic nomogram with a prognostic value similar to the AJCC system.

METHODS

Patients were identified from SEER database. Variables were selected by a backward stepwise selection method in a Cox regression model. A nomogram was used to predict cancer-specific survival rates for 3, 5 and 10 years in patients with GSCC. Several basic features of model validation were used to evaluate the performance of the survival model: consistency index (C-index), receiver operating characteristic (ROC) curve, calibration chart, net weight classification improvement (NRI), comprehensive discriminant improvement (IDI) and decision curve analysis (DCA).

RESULTS

Multivariate analyses revealed that age, race, marital status, insurance, AJCC stage, pathology grade and surgery were risk factors for survival. In particular, the C-index, the area under the ROC curve (AUC) and the calibration plots showed good performance of the nomogram. Compared to the AJCC system, NRI and IDI showed that the nomogram has improved performance. Finally, the nomogram's 3-year and 5-year and 10-year DCA curves yield net benefits higher than traditional AJCC, whether training set or a validation set.

CONCLUSION

We developed and validated the first GSCC prognosis nomogram, which has a better prognostic value than the separate AJCC staging system. Overall, the nomogram of this study is a valuable tool for clinical practice to consult patients and understand their risk for the next 3, 5 and 10 years.

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.

Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis

INTRODUCTION

Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.

METHODS

Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.

RESULTS

We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.

Competing-Risk Nomograms for Predicting the Prognosis of Patients With Infiltrating Lobular Carcinoma of the Breast

BACKGROUND

Infiltrating lobular carcinoma (ILC) is the second most common histologic subtype of breast cancer. We assessed the rates of cause-specific death in ILC patients with the aim of establishing competing-risk nomograms for predicting their prognosis.

PATIENTS AND METHODS

Data on ILC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function was used to calculate the cumulative incidence rates of cause-specific death, and Gray's test was applied to test the differences in cumulative incidence rates among groups. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard analysis method and established nomograms based on the results. Calibration curves and the concordance index were employed to validate the nomograms.

RESULTS

The study enrolled 11,361 patients. The 3-, 5-, and 10-year overall cumulative incidence rates for those who died of ILC were 3.1%, 6.2%, and 12.2%, respectively, whereas the rates for those who died from other causes were 3.2%, 5.8%, and 14.1%. Age, marriage, grade, size, regional node positivity, American Joint Committee on Cancer M stage, progesterone receptor, and surgery were independent prognostic factors for dying of ILC, whereas the independent prognostic factors for dying of other causes were age, race, marriage, size, radiation, and chemotherapy. The nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We applied competing-risk analysis to ILC patients based on the SEER database and established nomograms that perform well in predicting the cause-specific death rates at 3, 5, and 10 years after the diagnosis.

Development and validation of a prognostic nomogram for predicting the overall survival of myxofibrosarcoma patients: a large population-based study

Background

Although 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.

Methods

We 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.

Results

A 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).

Conclusions

A 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.

Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study

INTRODUCTION

The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis.

METHODS

Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram' s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

RESULTS

This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful.

CONCLUSIONS

The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.

Development and external validation of nomograms to predict sarcoma-specific death and disease progression after surgical resection of localized high-grade conventional primary central chondrosarcoma and dedifferentiated chondrosarcoma

AIMS

Our aim was to develop and validate nomograms that would predict the cumulative incidence of sarcoma-specific death (CISSD) and disease progression (CIDP) in patients with localized high-grade primary central and dedifferentiated chondrosarcoma.

METHODS

The study population consisted of 391 patients from two international sarcoma centres (development cohort) who had undergone definitive surgery for a localized high-grade (histological grade II or III) conventional primary central chondrosarcoma or dedifferentiated chondrosarcoma. Disease progression captured the first event of either metastasis or local recurrence. An independent cohort of 221 patients from three additional hospitals was used for external validation. Two nomograms were internally and externally validated for discrimination (c-index) and calibration plot.

RESULTS

In the development cohort, the CISSD at ten years was 32.9% (95% confidence interval (CI) 19.8% to 38.4%). Age at diagnosis, grade, and surgical margin were found to have significant effects on CISSD and CIDP in multivariate analyses. Maximum tumour diameter was also significantly associated with CISSD. In the development cohort, the c-indices for CISSD and CIDP at five years were 0.743 (95% CI 0.700 to 0.819) and 0.761 (95% CI 0.713 to 0.800), respectively. When applied to the validation cohort, the c-indices for CISSD and CIDP at five years were 0.839 (95% CI 0.763 to 0.916) and 0.749 (95% CI 0.672 to 0.825), respectively. The calibration plots for these two nomograms demonstrated good fit.

CONCLUSION

Our nomograms performed well on internal and external validation and can be used to predict CISSD and CIDP after resection of localized high-grade conventional primary central and dedifferentiated chondrosarcomas. They provide a new tool with which clinicians can assess and advise individual patients about their prognosis. Cite this article: Bone Joint J 2020;102-B(12):1752-1759.