A nomogram to predict prognosis in Ewing sarcoma of bone

Prognostic Modeling for Bone Sarcomas Based on a Large Prospective Cohort From a Tertiary Care Cancer Center in India

PURPOSE

Outcomes of adolescents and young adults (AYA) with bone sarcomas including osteosarcoma (OGS) and Ewing sarcoma (ES) are affected by various factors including inadvertent previous treatment and poor compliance. We aimed to develop a risk-scoring system derived and validated at a tertiary care cancer center in India.

METHODS

All AYA OGS and ES cases treated at our institute with OGS-12 and Ewing's family of tumors-2001 (EFT-2001) protocols from 2011 to 2021 and 2013 to 2018, respectively, were prospectively analyzed. Weighted scores provided to each prognostic variable on the basis of approximate ratios of the beta coefficients of each factor in the multivariable model were summated to divide patients into three clinically discriminatory risk groups, validated by applying separately to derivation, validation, and whole cohorts.

RESULTS

Among 606 (81.0%) of 748 AYA with nonmetastatic OGS, significant factors included in the prognostic model were failure to complete protocol (hazard ratio [HR], 2.65), previous treatment (HR, 2.93), necrosis <90% (HR, 1.63), joint involvement (HR, 2.0), and serum alkaline phosphatase >median (204 U/L; HR, 1.63). Of 104 (39.5%) of 263 AYA with ES, significant factors were failure to complete protocol (HR, 2.84), previous treatment (HR, 6.37), necrosis <100% (HR, 8.73), and tumor size >8 cm (HR, 2.64). For 142 (38.8%) of 366 AYA with metastatic OGS, significant factors were failure to complete protocol (HR, 5.29), metastases not amenable to local treatment (HR, 1.96), necrosis <90% (HR, 1.96), and >10 metastases (HR, 2.44). For 38 (43.6%) of 82 AYA with metastatic extremity ES, significant factors were failure to complete protocol (HR, 3.88) and metastases not amenable to local treatment (HR, 10.6).

CONCLUSION

We developed simple, effective prognostic models for AYA with bone sarcomas with specific potential relevance for low- and middle-income countries.

Lung and bone metastases patterns in Ewing sarcoma: Chemotherapy improves overall survival

Ewing sarcoma (ES) is a small round cell malignancy, mainly in the bone tissue, followed by the soft tissue. Lung metastases (LM) and bone metastases (BM) are the most common types of metastases. From 2010 to 2018, the Surveillance, Epidemiology, and End Results database diagnosed 242 cases of ES with LM, 186 cases of ES with BM, and 74 cases of ES with LM and BM. Univariate and multivariate logistic regression analyses were used to determine the risk factors for LM and/or BM, and Kaplan-Meier curves and Cox regression analysis were used to determine the prognostic factors for LM and/or BM. Tumor size ≥50 mm, N1 stage, BM, liver metastases, and surgical treatment were significantly correlated with LM; tumor size >100 mm, brain metastases, LM, surgical treatment, and chemotherapy were significantly correlated with BM; female, N1 stage, brain metastases, liver metastases, and surgical treatment were significantly correlated with LM and BM. Older age, BM, higher T stage, no surgical treatment, and no chemotherapy were harmful to the survival of ES patients with LM; older age, female, LM, and no chemotherapy were harmful to the survival of ES patients with BM; older age and no chemotherapy were harmful to the survival of ES patients with LM and BM. Larger tumor size, N1 stages, and organ metastases were significantly associated with ES patients with LM and/or BM. Chemotherapy is effective in improving the survival.

The development and external validation of a web-based nomogram for predicting overall survival with Ewing sarcoma in children

Background

Ewing sarcoma remains the second most prevalent primary aggressive bone tumor in teens and young adults. The aim of our study was to develop and validate a web-based nomogram to predict the overall survival for Ewing sarcoma in children.

Methods

A total of 698 patients, with 640 cases from the Surveillance, Epidemiology, and End Results (the training set) and 58 cases (the external validation set), were included in this study. Cox analyses were carried out to determine the independent prognostic indicators, which were further included to establish a web-based nomogram. The predictive abilities were tested through the concordance index, calibration curve, decision curve analysis, and area under the receiver operating characteristic curve.

Results

As suggested by univariate and multivariate Cox analyses, age, primary site, tumor size, metastasis stage (M stage), and chemotherapy were included as the independent predictive variables. The area under the receiver operating characteristic curve values, calibration curves, concordance index, and decision curve analysis from training and validation groups suggested the model has great clinical applications.

Conclusion

We developed a convenient and precise web-based nomogram to evaluate overall survival for Ewing sarcoma in children. The application of this nomogram would assist physicians and patients in making decisions.

The Clinical Characteristics, Risk Classification System, and Web-Based Nomogram for Primary Spinal Ewing Sarcoma: A Large Population-Based Cohort Study

BACKGROUND

The goal of this study was to determine the clinical characteristics of patients with primary spinal Ewing sarcoma (PSES) and to create a prognostic nomogram.

METHODS

Clinical information related to patients diagnosed with PSES between 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified using univariate and multivariate Cox analyses to construct nomograms predicting overall survival in patients with PSES. Calibration curves and receiver operating characteristic curves were used to assess the model's prediction accuracy, while decision curve analysis was used to assess the model's clinical utility.

RESULTS

The overall number of 314 patients with PSES were screened from the SEER database between 2004 and 2015. Race, chemotherapy, age, and disease stage were found to be independent predictive factors for overall survival in both univariate and multivariate Cox analyses. The training and validation cohorts' calibration curves, receiver operating characteristic curves, and decision curve analysis showed that the nomogram has strong discrimination and clinical value. Furthermore, a new risk classification system has been constructed that can divide all patients into 2 risk groups.

CONCLUSIONS

Based on a broad population, the research demonstrates statistical evidence for the clinical features and prognostic variables of patients with PSES. The constructed prognostic nomogram provides a more precise prediction of prognosis for PSES patients.

Overall survival comparison between pediatric and adult Ewing sarcoma of bone and adult nomogram construction: a large population-based analysis

Background

Ewing sarcoma (ES) is a common primary bone tumor in children. Our study aimed to compare overall survival (OS) between pediatric and adult bone ES patients, identify independent prognostic factors and develop a nomogram for predicting OS in adult patients with ES of bone.

Methods

We retrospectively analyzed data for the 2004-2015 period from the Surveillance, Epidemiology, and End Results (SEER) database. To guarantee well-balanced characteristics between the comparison groups, propensity score matching (PSM) was used. Kaplan-Meier (KM) curves were used to compare OS between pediatric and adult patients with ES of bone. Univariate and multivariate Cox regression analyses were used to screen independent prognostic factors for ES of bone, and a prognostic nomogram was constructed by using the factors identified. The prediction accuracy and clinical benefit were evaluated using receiver operating characteristic (ROC) curves, areas under the curves (AUCs), calibration curves, and decision curve analysis (DCA).

Results

Our results showed that adult ES patients had lower OS than younger ES patients. Age, surgery, chemotherapy, and TNM stage were independent risk factors for bone ES in adults and were used to develop a nomogram. AUCs for 3-, 5-, and 10-year OS were 76.4 (67.5, 85.3), 77.3 (68.6, 85.9) and 76.6 (68.6, 84.5), respectively. Calibration curves and DCA results indicated excellent performance for our nomogram.

Conclusion

We found that ES pediatric patients have better OS than adult ES patients, and we constructed a practical nomogram to predict the 3-, 5- and 10-year OS of adult patients with ES of bone based on independent prognostic factors (age, surgery, chemotherapy, T stage, N stage and M stage).

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 clinical characteristics, novel predictive tool, and risk classification system for primary Ewing sarcoma patients that underwent chemotherapy: A large population-based retrospective cohort study

BACKGROUND

This study aims to determine the independent prognostic predictors of cancer-specific survival (CSS) in patients with primary Ewing sarcoma (ES) that underwent chemotherapy and create a novel prognostic nomogram and risk stratification system.

METHODS

Demographic and clinicopathologic characteristics related to patients with primary ES that underwent chemotherapy between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. CSS was the primary endpoint of this study. First, independent prognostic predictors of CSS identified from univariate and multivariate Cox regression analyses were used to construct a prognostic nomogram for predicting 1-, 3-, and 5-year CSS of patients with primary ES that underwent chemotherapy. Then, calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the nomogram's prediction accuracy, while decision curve analysis (DCA) was used to evaluate the nomogram's clinical utility. Finally, a mortality risk stratification system was constructed for this subpopulation.

RESULTS

A total of 393 patients were included in this study. Age, tumor size, bone metastasis, and surgery were independent prognostic predictors of CSS. The calibration curves, ROC, and DCA showed that the nomogram had excellent discrimination and clinical value, with the 1-, 3-, and 5-year AUCs higher than 0.700. Moreover, the mortality risk stratification system could effectively divide all patients into three risk subgroups and achieve targeted patient management.

CONCLUSIONS

Based on the SEER database, a novel prognostic nomogram for predicting 1-, 3-, and 5- year CSS in patients with primary ES that underwent chemotherapy has been constructed and validated. The nomogram showed relatively good performance, which could be used in clinical practice to assist clinicians in individualized treatment strategies.

Development and validation of a nomogram to predict Chinese breast cancer risk based on clinical serum biomarkers

Background: This study investigated and compared clinical serum biomarkers and developed a diagnostic nomogram for breast cancer. Methods: A total of 1224 breast cancer and 1280 healthy controls were enrolled. Univariate and multivariate analyses were performed to identify factors and a nomogram was developed. Discrimination, accuracy and clinical utility values were evaluated by receiver operating characteristic, Hosmer-Lemeshow, calibration plots, decision curve analysis and clinical impact plots. Results: carcinoembryonic antigen, CA125, CA153, lymphocyte-to-monocyte ratio, platelet-to-lymphocyte ratio, fibrinogen and platelet distributing width were effectively identified to predict breast cancer. The nomogram showed the area under the curve of 0.708 and 0.710 in the training and validation set. Calibration plots, Hosmer-Lemeshow, decision curve analysis and clinical impact plots confirmed great accuracy and clinical utility. Conclusion: We developed and validated a nomogram that is effectively used for risk prediction of Chinese breast cancer.

Surgical efficacy and survival prediction of patients with unspecified malignant bone tumors

Background

The surgical efficacy and prognostic outcomes of patients with unspecific malignant bone tumors (UMBTs) remain unclear. The study is to address: 1) What are the clinicopathological features and prognostic determinants for patients with UMBTs? 2) Can a nomogram be developed for clinicians to predict the short and long-term outcomes for individuals with UMBTs? 3) Does surgery improve outcomes for UMBT patients who received radiotherapy or chemotherapy after balancing the confounding bias?

Methods

400 UMBT patients were filtrated from the Surveillance, Epidemiology, and End Results database to assess the clinicopathological features, treatments, and factors affecting prognosis. The optimal cutoff values of continuous variables were identified by the x-tile software. Kaplan-Meier method and multivariate Cox proportional hazard modeling were performed to evaluate the independent prognostic factors. Nomogram was further developed by using R software with rms package. The surgical efficacy was further assessed for patients receiving radiotherapy or chemotherapy after performing propensity score matching.

Results

The enrolled cohort included 195 (48.8%) female and 205 (51.2%) male patients. The 2- and 5-year cancer-specific survival (CSS) and overall survival (OS) rate were 58.2 ± 3.0%, 46.8 ± 3.2%, and 46.5 ± 2.6%, 34.4 ± 2.5%, respectively. Nomogram was finally developed for CSS and OS according to the identified independent factors: age, tumor extent, primary tumor surgery, tumor size, and pathology grade. For UMBT patients who received radiotherapy or chemotherapy, surgical intervention was associated with better CSS (pr = 0.003, pc = 0.002) and OS (pr = 0.035, pc = 0.002), respectively.

Conclusions

Nomogram was developed for individual UMBT patient to predict short and long-term CSS and OS rate, and more external patient cohorts are warranted for validation. Surgery improves outcomes for UMBT patients who received either radiotherapy or chemotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10153-x.

TrkC, a novel prognostic marker, induces and maintains cell survival and metastatic dissemination of Ewing sarcoma by inhibiting EWSR1-FLI1 degradation

Upregulation of EWSR1-FLI1 expression has been associated with invasiveness, induced cell survival, metastatic dissemination, and acquisition of self-renewal traits in Ewing sarcoma (ES). Although existing evidence implies that TrkC expression is linked to the pathogenesis of other cancer types, its role and the mechanism behind its correlation with EWSR1-FLI1 in the pathogenesis of ES remain unclear. In this study, we uncovered a novel physiological role of TrkC as a key regulator of EWSR1-FLI1 involved in the survival and metastatic dissemination of ES. TrkC was observed to be frequently overexpressed in human metastatic ES cells in vitro and in vivo, facilitating enhanced survival, tumorigenicity, and metastasis of ES cells. TrkC-mediated metastasis of ES cells was induced by the inhibition of the proteasomal degradation of EWSR1-FLI1 via the TrkC/EWSR1-FLI1 complex, which subsequently enabled the induction of the target proteins, EGR2 and NKX2.2. Moreover, TrkC significantly inhibited tumor suppressor activity of TGF-β through reduction of the mRNA expression of one of its receptors, TGFBR2 via TrkC-induced stabilization of EWSR1-FLI1. Furthermore, loss of TrkC expression inhibited tumor growth and metastasis in experimental mouse models. This study is the first to report the involvement and functional role of TrkC in the pathogenesis of ES, suggesting important implications for understanding the alterations of TrkC in Ewing tumors.

A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study

OBJECTIVE

The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES).

METHODS

Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 2010-2016 were extracted, and the data after exclusion of vacant terms was used as the training set (n=767). Prediction models predicting patients' overall survival (OS) at 1 and 3 years were created by cox regression analysis and visualized using Nomogram and web calculator. Multicenter data from four medical institutions were used as the validation set (n=51), and the model consistency was verified using calibration plots, and receiver operating characteristic (ROC) verified the predictive ability of the model. Finally, a clinical decision curve was used to demonstrate the clinical utility of the model.

RESULTS

The results of multivariate cox regression showed that age, , bone metastasis, tumor size, and chemotherapy were independent prognostic factors of ES patients. Internal and external validation results: calibration plots showed that the model had a good agreement for patient survival at 1 and 3 years; ROC showed that it possessed a good predictive ability and clinical decision curve proved that it possessed good clinical utility.

CONCLUSIONS

The tool built in this paper to predict 1- and 3-year survival in ES patients ( https://drwenleli0910.shinyapps.io/EwingApp/ ) has a good identification and predictive power.

A Novel Defined Hypoxia-Related Gene Signature for Prognostic Prediction of Patients With Ewing Sarcoma

The therapeutic strategy of Ewing sarcoma (EWS) remains largely unchanged over the past few decades. Hypoxia is reported to have an impact on tumor cell progression and is regarded as a novel potential therapeutic target in tumor treatment. This study aimed at developing a prognostic gene signature based on hypoxia-related genes (HRGs). EWS patients from GSE17674 in the GEO database were analyzed as a training cohort, and differently expressed HRGs between tumor and normal samples were identified. The univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used in this study. A total of 57 EWS patients from the International Cancer Genome Consortium (ICGC) database were set as the validation cohort. A total of 506 differently expressed HRGs between tumor and normal tissues were identified, among which 52 were associated with the prognoses of EWS patients. Based on 52 HRGs, EWS patients were divided into two molecular subgroups with different survival statuses. In addition, a prognostic signature based on 4 HRGs (WSB1, RXYLT1, GLCE and RORA) was constructed, dividing EWS patients into low- and high-risk groups. The 2-, 3- and 5-years area under the receiver operator characteristic curve of this signature was 0.913, 0.97 and 0.985, respectively. It was found that the survival rates of patients in the high-risk group were significantly lower than those in the low-risk group (p < 0.001). The risk level based on the risk score could serve as an independent clinical factor for predicting the survival probabilities of EWS patients. Additionally, antigen-presenting cell (APC) related pathways and T cell co-inhibition were differently activated in two risk groups, which may result in different prognoses. CTLA4 may be an effective immune checkpoint inhibitor to treat EWS patients. All results were verified in the validation cohort. This study constructed 4-HRGs as a novel prognostic marker for predicting survival in EWS patients.

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.

Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface

Background. This study aimed at establishing and validating a quantitative and visual prognosis model of Ewing Sarcoma (E.S.) via a nomogram. This model was developed to predict the risk of lung metastasis (L.M.) in patients with E.S. to provide a practical tool and help in clinical diagnosis and treatment. Methods. Data of all patients diagnosed with Ewing sarcoma between 2010 and 2016 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A training dataset from the enrolled cohorts was built (n = 929). Predictive factors for L.M. were identified based on the results of multivariable logistic regression analyses. A nomogram model and a web calculator were constructed based on those key predictors. A multicenter dataset from four medical institutions was established for model validation (n = 51). The predictive ability of the nomogram model was evaluated by the receiver operating characteristic (ROC) curve and calibration plot. Decision curve analysis (DCA) was applied to explain the accuracy of the nomogram model in clinical practice. Results. Five independent factors, including survival time, surgery, tumor (T) stage, node (N) stage, and bone metastasis, were identified to develop a nomogram model. Internal and external validation indicated significant predictive discrimination: the area under the ROC curve (AUC) value was 0.769 (95% CI: 0.740 to 0.795) in the training cohort and 0.841 (95% CI: 0.712 to 0.929) in the validation cohort, respectively. Calibration plots and DCA presented excellent performance of the nomogram model with great clinical utility. Conclusions. In this study, a nomogram model was constructed and validated to predict L.M. in patients with E.S. for medical human-computer interface—a web calculator (https://drliwenle.shinyapps.io/LMESapp/). This practical tool could help clinicians make better decisions to provide precision prognosis and treatment for patients with E.S.

Development and validation of a prognostic score at baseline diagnosis for Ewing sarcoma family of tumors: a retrospective single institution analysis of 860 patients

INTRODUCTION

Prognostic scores in Ewing sarcoma including baseline clinical and laboratory characteristics are necessary for pre-treatment risk stratification. In this study, we formulated and validated a prognostic model for baseline risk categorization in Ewing sarcoma.

MATERIALS AND METHODS

A retrospective single-institutional study was conducted on Ewing sarcoma patients treated uniformly between January 2003 and December 2018. Baseline clinical/pathological characteristics and survival outcomes were noted from medical records. The cohort was randomised into a derivation and validation cohort. A prognostic score was formulated by including independent prognostic factors from the derivation cohort by multivariable analysis. The prognostic model was validated in the validation cohort along with estimation of its predictive ability.

RESULTS

A total of 860 patients were included with 40.3% having baseline metastases. Tumor diameter >5 cm (HR 2.04; P<0.001; score 2), baseline metastases (HR 2.33; P<0.001, score 2), and total leucocyte count >11000/mm3 (HR 1.44; P=0.015; score 1) were independent predictors of overall survival in derivation cohort and included for prognostic score calculation. Patients were categorized into low (score 0), intermediate (score 1-3) and high-risk (score 4-5) groups. Harrell's c-indexes of the model were 0.625, 0.622 and 0.624 in the derivation, validation and whole cohort respectively. The timed AUC of ROC of the prognostic score-group for 5-year survival was 0.72, 0.71 and 0.73 in the derivation, validation and whole cohort respectively.

CONCLUSIONS

We have formulated and validated a prognostic score for Ewing sarcoma incorporating baseline clinical and laboratory parameters, with fair predictive ability for risk stratification and facilitating risk-adapted personalized therapy.

New nomograms to predict overall and cancer-specific survival of angiosarcoma

Objective

This study was designed to establish and validate promising and reliable nomograms for predicting the survival of angiosarcoma (AS) patients.

Methods

The Surveillance, Epidemiology, and End Results database was queried to collect the clinical information of 785 AS patients between 2004 and 2015. Data were split into a training cohort (n = 549) and a validation cohort (n = 236) without any preference. Univariate Cox and multivariate Cox regression analyses were performed to analyze the clinical parameters. Independent prognostic factors were then identified. Two nomograms were constructed to predict overall survival (OS) and cancer‐specific survival (CSS) at 3 and 5 years. Finally, the models were evaluated using concordance indices (C‐indices), calibration plots, and decision curve analysis (DCA).

Results

Based on the inclusion and exclusion criteria, 785 individuals were included in this analysis. Univariate and multivariate Cox regression analyses revealed that age, tumor size, and stage were prognostic factors independently associated with the OS of AS. Tumor site, tumor size, and stage were associated with the CSS of AS. Based on the statistical results and clinical significance of variables, nomograms were built. The nomograms for OS and CSS had C‐indices of 0.666 and 0.654, respectively. The calibration curves showed good agreement between the predictive values and the actual values. DCA also indicated that the nomograms were clinically useful.

Conclusion

We established nomograms with good predictive ability that could provide clinicians with better predictions about the clinical outcomes of AS patients.

Using machine learning techniques predicts prognosis of patients with Ewing sarcoma

Ewing sarcoma is one of the most common types of malignant bone tumor in children and adolescents. However, to our limited knowledge, no study exists that uses machine learning to create algorithms for the prediction of survivorship for Ewing sarcoma. About 2332 patients with Ewing sarcoma between 1975 and 2016 in the United States were identified from Surveillance, Epidemiology, and End Results (SEER) program. All patients in the data set were randomly assigned into the training set and the testing set, at a 2:8 ratio. In the training set, boosted decision tree, support vector machine, nonparametric random forest method, and neural network models were developed to predict the 5-year survivorship. The overall survival rate in 5-year follow-up of this patient cohort is 60.72%. With respect to the algorithms for both cancer specific survival and overall survival, there was slight superiority in our performance metrics favoring the random forest method over the other models for survival prediction, with 77/83% sensitivity and 91/94% specificity, respectively. The random forest method was incorporated into a freely available web-based application. This application can be accessed through https://zryan.shinyapps.io/EwingSarcoma/. Clinical Significance: To the best of our knowledge, this is the first available predictive model for predicting survival in Ewing sarcoma based on machine-learning algorithms. This study may provide orthopedic surgeons with an easily accessible prediction tool when dealing with patients suffering from Ewing sarcoma.

An Efficient Nomogram to Predict Overall Survival of Patients with Pediatric Ewing's Sarcoma: A Population-Based Study

Background

The objective of our study was to develop and validate a nomogram to predict the overall survival (OS) of patients with pediatric Ewing’s sarcoma (PES).

Methods

Age, gender, race, tumor stage, tumor size, tumor site, treatment method, and survival time were collected from patients diagnosed with PES between 2004 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database. A total of 772 patients were randomly allocated to a training dataset (n = 579) and a validation dataset (n = 193). Then, univariate and multivariate analyses were performed to determine the prognostic effect of the selected variables. A nomogram was constructed to estimate the OS and it was further assessed using the concordance index (C-index), calibration curves, and receiver operating characteristic (ROC).

Results

Age, race, tumor size, and tumor stage were included in the nomogram. The C-index was 0.77 in the OS for the training dataset. The C-index for the validation dataset of the OS prediction was 0.75. Calibration plots and ROC curves showed excellent predictive accuracy.

Conclusion

Age, race, tumor stage, and tumor size were independent prognostic factors for patients with PES. The nomogram showed an accurate and reliable prognostic performance for PES patients.

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.

Ewing family tumors of the appendicular skeleton: a retrospective analysis of prognostic factors

PURPOSE

Authors retrospectively analyzed possible prognostic factors in a series of patients affected by Ewing sarcoma of extremities (eEWS) and treated over a 20-year period at a single institution.

METHODS

Between 1997 and 2017, 88 bone eEWS were treated at our institution. Staging, age, gender, tumoral volume, local treatment, surgical margins, post-ChT necrosis were investigated for prognostic correlation with overall survival (OS) and event-free survival (EFS). Median follow-up was 74 months (1-236).

RESULTS

Staging of disease correlated with OS (81% vs 59%, p = 0.01) and not with EFS (68% vs 57%, p = 0.28) in localized vs metastatic eEWS at presentation. Age ≥ 14 years (p = 0.002) and volume ≥ 100 cm3 (p = 0.04) were significant negative prognostic factors. No difference was found in local treatment: OS was 76% vs 63% (p = 0.33), while EFS was 68% vs 49% (p = 0.06) after surgery alone or surgery + radiotherapy, respectively. Regarding surgical margins, OS was 76% vs 38% (p = 0.14), and EFS was 65% vs 33% (p = 0.14) in adequate vs not adequate, respectively. OS was 86% and 68% in good and poor responders, respectively (p = 0.13).

CONCLUSION

In eEWS, metastatic disease at presentation, age > 14 years and tumoral volume > 100 cm³ are negative prognostic factors. Intensified adjuvant ChT can improve prognosis in poor responders and metastatic patients.

A Nomogram Predicting Overall and Cancer-Specific Survival of Patients with Primary Bone Lymphoma: A Large Population-Based Study

We aimed to develop a nomogram for evaluating the overall survival (OS) and cancer-specific survival (CSS) in patients with primary bone lymphoma (PBL). Patients diagnosed with PBL between 2007 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to the training cohort and validation cohort (2 : 1). The nomogram was developed by the training cohort and validated by the validation cohort using the concordance index (C-index), calibration plots, and decision curve analyses (DCAs). The C-index for CSS and OS prediction in the training cohort were 0.76 and 0.77, respectively; in the validation cohort, they were 0.76 and 0.79, respectively. The calibration curve showed good consistency between nomogram prediction and actual survival. The DCA indicated obvious net benefits of the new predictive model. The nomogram showed favorable applicability and accuracy, and it will be a reliable tool for predicting OS and CSS in patients with PBL.

Development of machine learning model algorithm for prediction of 5-year soft tissue myxoid liposarcoma survival

BACKGROUND

Predicting survival in myxoid liposarcoma (MLS) patients is very challenging given its propensity to metastasize and the controversial role of adjuvant therapy. The purpose of this study was to develop a machine-learning algorithm for the prediction of survival at five years for patients with MLS and externally validate it using our institutional cohort.

METHODS

Two databases, the surveillance, epidemiology, and end results program (SEER) database and an institutional database, were used in this study. Five machine learning models were created based on the SEER database and performance was rated using the TRIPOD criteria. The model that performed best on the SEER data was again tested on our institutional database.

RESULTS

The net-elastic penalized logistic regression model was the best according to our performance indicators. This model had an area under the curve (AUC) of 0.85 when compared to the SEER testing data and an AUC of 0.76 when tested against institutional database. An application to use this calculator is available at https://sorg-apps.shinyapps.io/myxoid_liposarcoma/.

CONCLUSION

MLS is a soft-tissue sarcoma with adjunct treatment options that are, in part, decided by prognostic survival. We developed the first machine-learning predictive algorithm specifically for MLS using the SEER registry that retained performance during external validation with institutional data.

Lung Metastasis Probability in Ewing Sarcoma: A Nomogram Based on the SEER Database

Background. Up to now, an accurate nomogram to predict the lung metastasis probability in Ewing sarcoma (ES) at initial diagnosis is lacking. Our objective was to construct and validate a nomogram for the prediction of lung metastasis in ES patients. Methods. A total of 1157 patients with ES from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively collected. The predictors of lung metastasis were identified via the least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis. The discrimination and calibration of the nomogram were validated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical usefulness and net benefits of the prediction model. Results. Factors including age, tumor size, primary site, tumor extension, and other site metastasis were identified as the ultimate predictors for the nomogram. The calibration curves for the training and validation cohorts both revealed good agreement, and the Hosmer–Lemeshow test identified that the model was well fitted (p > 0.05). In addition, the area under the ROC curve (AUC) values in the training and validation cohorts were 0.732 (95% confidence interval, CI: 0.607–0.808) and 0.741 (95% CI: 0.602–0.856), respectively, indicating good predictive discrimination. The DCA showed that when the predictive metastasis probability was between 1% and 90%, the nomogram could provide clinical usefulness and net benefit. Conclusion. The nomogram constructed and validated by us could provide a convenient and effective tool for clinicians that can improve prediction of the probability of lung metastasis in patients with ES at initial diagnosis.

Prediction of Minor Salivary Gland Carcinoma: A Novel Nomogram and Risk Classification System for Overall Survival and Cancer-Specific Survival

OBJECTIVE

Minor salivary gland carcinoma (MiSGC) is rare, and the understanding of this disease is insufficient. This study aimed to identify independent risk factors and develop a nomogram for evaluating the overall survival (OS) and cancer-specific survival (CSS) of patients with MiSGC.

STUDY DESIGN

Retrospective cohort study.

SETTING

SEER database (Surveillance, Epidemiology, and End Results).

SUBJECTS AND METHODS

We collected data from patients diagnosed with MiSGC between 2004 and 2015 from the SEER database. According to patient registration, all patients were randomly allocated to training sets and validation sets (2:1). Then, Kaplan-Meier product limit curves and Cox proportional hazard regressions were performed to estimate the prognostic effect of variables. Nomograms based on Cox proportional hazard regressions were established to estimate 3- and 5-year OS and CSS. Finally, the nomogram was developed by the training set, and validation was performed with the concordance index, calibration curves, and decision curve analyses.

RESULTS

In total, 1787 MiSGC cases were registered in SEER. The concordance index for internal validation of OS and CSS prediction was 0.842 and 0.816; that of external validation was 0.871 and 0.831. The calibration plots showed good consistency between nomogram prediction and actual survival. The decision curve analysis showed substantial net benefits of the new predictive model.

CONCLUSIONS

We constructed nomograms and a corresponding risk classification system predicting the OS and CSS of patients with MiSGC. These tools can generate simple-to-use clinical risk grouping and determine the relationship between adjuvant therapy and active surveillance.

A Nomogram and a Risk Classification System Predicting the Cancer-Specific Survival of Patients With Initially-Diagnosed Osseous Spinal and Pelvic Tumors

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

Our goal was to provide a predictive model and a risk classification system that predicts cancer-specific survival (CSS) from spinal and pelvic tumors.

SUMMARY OF BACKGROUND DATA

Primary bone tumors of the spinal and pelvic are rare, thus limiting the understanding of the manifestations and survival from these tumors. Nomograms are the graphical representation of mathematical relationships or laws that accurately predict individual survival.

METHODS

A total of 1033 patients with spinal and pelvic bone tumors between 2004 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox analysis was used on the training set to select significant predictors to build a nomogram that predicted 3- and 5-year CSS. We validate the precision of the nomogram by discrimination and calibration, and the clinical value of nomogram was assessed by making use of a decision curve analyses (DCA).

RESULTS

Data from 1033 patients with initially-diagnosed spinal and pelvic tumors were extracted from the SEER database. Multivariate analysis of the training cohort, predictors included in the nomogram were age, pathological type, tumor stage, and surgery. The value of C-index was 0.711 and 0.743 for the internal and external validation sets, respectively, indicating good agreement with actual CSS. The internal and external calibration curves revealed good correlation of CSS between the actual observation and the nomogram. Then, the DCA showed greater net benefits than that of treat-all or treat-none at all time points. A novel risk grouping system was established for CSS that can readily divide all patients into three distinct risk groups.

CONCLUSION

The proposed nomogram obtained more precision prognostic prediction for patients with initially-diagnosed primary spinal and pelvic tumors.

LEVEL OF EVIDENCE

3.

Construction and Validation of Nomograms for Predicting the Prognosis of Uterine Leiomyosarcoma: A Population-Based Study

BACKGROUND Uterine leiomyosarcoma (uLMS) is a rare female malignancy with poor survival rates. The objective of this study was to construct prognostic nomograms for predicting the prognosis of women with uLMS. MATERIAL AND METHODS Patients with uLMS diagnosed between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The essential clinical predictors were identified via univariate and multivariate Cox analysis models. Nomograms were constructed to predict the 3- and 5-year cancer-specific survival (CSS) and overall survival (OS) rates. Concordance index (C-index) and calibration plots were constructed to validate the predictive performance of nomograms. RESULTS We enrolled 1448 patients with uLMS from the SEER database, with 1016 categorized into a training set and 432 categorized into a validation set. In multivariate analysis of the training set, predictors including age, disease stage, histological grade, tumor size, and surgery type were found to be associated with OS and CSS. Race and chemotherapy were only associated with OS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of uLMS patients. The C-index and calibration curves also showed the satisfactory performance of these nomograms for prediction of prognosis. CONCLUSIONS The developed nomograms are useful tools for precisely analyzing the prognosis of uLMS patients, which could help clinicians in making personalized survival predictions and assessing individualized clinical options.

Predictive Analytics in Spine Oncology Research: First Steps, Limitations, and Future Directions

The potential of big data analytics to improve the quality of care for patients with spine tumors is significant. At this moment, the application of big data analytics to oncology and spine surgery is at a nascent stage. As such, efforts are underway to advance data-driven oncologic care, improve patient outcomes, and guide clinical decision making. This is both relevant and critical in the practice of spine oncology as clinical decision making is often made in isolation looking at select variables deemed relevant by the physician. With rapidly evolving therapeutics in surgery, radiation, interventional radiology, and oncology, there is a need to better develop decision-making algorithms utilizing the vast data available for each patient. The challenges and limitations inherent to big data analyses are presented with an eye towards future directions.

A nomogram to predict vascular invasion before resection of colorectal cancer

Vascular invasion (VI) is an important feature for systemic recurrence and an indicator for the application of adjuvant therapy in colorectal cancer (CRC). Preoperative knowledge of VI is important in determining whether adjuvant therapy is necessary, as well as the adequacy of surgical resection. In the present study, a predictive nomogram for VI in patients with CRC was constructed. The prediction model consisted of 664 eligible patients with CRC, who were divided into a training set (n=468) and a validation set (n=196). Data were collected between August 2013 and April 2018. The feature selection model was established using the least absolute shrinkage and selection operator regression model. Multivariable logistic regression analysis was used to construct the predictive nomogram. The performance of the nomogram was evaluated by calibration, discrimination and clinical usefulness. Differentiation, computed tomography (CT)-based on N stage (CT N stage), hemameba and tumor distance from the anus (cm) were integrated into the nomogram. The nomogram exhibited good discrimination, with an area under the curve (AUC) of 0.731 and good calibration. Application of the nomogram in the validation cohort showed acceptable discrimination, with an AUC of 0.710 and good calibration. Decision curve analysis revealed that the nomogram was clinically useful. These findings suggests, to the best of our knowledge, that this may be the first nomogram for individual preoperative prediction of VI in patients with CRC, which may promote preoperative optimization strategies for this selected group of patients.

Development and Validation of a Prognostic Nomogram for Extremity Soft Tissue Leiomyosarcoma

Background: Extremity soft tissue leiomyosarcoma (LMS) is a rare disease with a poor prognosis. The aim of this study is to develop nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity soft tissue LMS.

Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, 1,528 cases of extremity soft tissue LMS diagnosed between 1983 and 2015 were included. Cox proportional hazards regression modeling was used to analyze prognosis and obtain independent predictors. The independent predictors were integrated to develop nomograms predicting 5- and 10-year OS and CSS. Nomogram performance was evaluated by a concordance index (C-index) and calibration plots using R software version 3.5.0.

Results: Multivariate analysis revealed that age ≥60 years, high tumor grade, distant metastasis, tumor size ≥5 cm, and lack of surgery were significantly associated with decreased OS and CSS. These five predictors were used to construct nomograms for predicting 5- and 10-year OS and CSS. Internal and external calibration plots for the probability of 5- and 10-year OS and CSS showed excellent agreement between nomogram prediction and observed outcomes. The C-index values for internal validation of OS and CSS prediction were 0.776 (95% CI 0.752–0.801) and 0.835 (95% CI 0.810–0.860), respectively, whereas those for external validation were 0.748 (95% CI 0.721–0.775) and 0.814 (95% CI 0.785–0.843), respectively.

Conclusions: The proposed nomogram is a reliable and robust tool for accurate prognostic prediction in patients with extremity soft tissue LMS.