Can a Nomogram Help to Predict the Overall and Cancer-specific Survival of Patients With Chondrosarcoma?

Development and validation of a deep learning-based survival prediction model for pediatric glioma patients: A retrospective study using the SEER database and Chinese data

OBJECTIVE

Develop a time-dependent deep learning model to accurately predict the prognosis of pediatric glioma patients, which can assist clinicians in making precise treatment decisions and reducing patient risk.

STUDY DESIGN

The study involved pediatric glioma patients from the Surveillance, Epidemiology, and End Results (SEER) Registry (2000-2018) and Tangdu Hospital in China (2010-2018) within specific time frames. For training, we selected two neural network-based algorithms (DeepSurv, neural multi-task logistic regression [N-MTLR]) and one ensemble learning-based algorithm (random survival forest [RSF]). Additionally, a multivariable Cox proportional hazard (CoxPH) model was developed for comparison purposes. The SEER dataset was randomly divided into 80 % for training and 20 % for testing, while the Tangdu Hospital dataset served as an external validation cohort. Super-parameters were fine-tuned through 1000 repeated random searches and 5-fold cross-validation on the training cohort. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). Furthermore, the accuracy of predicting survival at 1, 3, and 5 years was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the area under the ROC curves (AUC). The generalization ability of the model was assessed using the C-index of the Tangdu Hospital data, ROC curves for 1, 3, and 5 years, and AUC values. Lastly, decision curve analysis (DCA) curves for 1, 3, and 5-year time frames are provided to assess the net benefits across different models.

RESULTS

A total of 9532 patients with pediatric glioma were included in this study, comprising 9274 patients from the SEER database and 258 patients from Tangdu Hospital in China. The average age at diagnosis was 9.4 ± 6.2 years, and the average survival time was 96 ± 66 months. Through comprehensive performance comparison, the DeepSurv model demonstrated the highest effectiveness, with a C-index of 0.881 on the training cohort. Furthermore, it exhibited excellent accuracy in predicting the 1-year, 3-year, and 5-year survival rates (AUC: 0.903-0.939). Notably, the DeepSurv model also achieved remarkable performance and accuracy on the Chinese dataset (C-index: 0.782, AUC: 0.761-0.852). Comprehensive analysis of DeepSurv, N-MTLR, and RSF revealed that tumor stage, radiotherapy, histological type, tumor size, chemotherapy, age, and surgical method are all significant factors influencing the prognosis of pediatric glioma. Finally, an online version of the pediatric glioma survival predictor based on the DeepSurv model has been established and can be accessed through https://pediatricglioma-tangdu.streamlit.app.

CONCLUSIONS

The DeepSurv model exhibits exceptional efficacy in predicting the survival of pediatric glioma patients, demonstrating strong performance in discrimination, calibration, stability, and generalization. By utilizing the online version of the pediatric glioma survival predictor, which is based on the DeepSurv model, clinicians can accurately predict patient survival and offer personalized treatment options.

Japanese orthopaedic association (JOA) clinical practice guidelines on the management of malignant bone tumors - Secondary publication

BACKGROUND

In Japan, there are currently no general guidelines for the treatment of primary malignant bone tumors. Therefore, the Japanese Orthopaedic Association established a committee to develop guidelines for the appropriate diagnosis and treatment of primary malignant bone tumors for medical professionals in clinical practice.

METHODS

The guidelines were developed in accordance with "Minds Clinical Practice Guideline Development Handbook 2014″ and "Minds Clinical Practice Guideline Development Manual 2017". The Japanese Orthopaedic Association's Bone and Soft Tissue Tumor Committee established guideline development and systematic review committees, drawing members from orthopedic specialists leading the diagnosis and treatment of bone and soft tissue tumors. Pediatricians, radiologists, and diagnostic pathologists were added to both committees because of the importance of multidisciplinary treatment. Based on the diagnosis and treatment algorithm for primary malignant bone tumors, important decision-making points were selected, and clinical questions (CQ) were determined. The strength of recommendation was rated on two levels and the strength of evidence was rated on four levels. The recommendations published were selected based on agreement by 70% or more of the voters.

RESULTS

The guideline development committee examined the important clinical issues in the clinical algorithm and selected 22 CQs. The systematic review committee reviewed the evidence concerning each CQ and a clinical value judgment was added by experts. Eventually, 25 questions were published and the text of each recommendation was determined.

CONCLUSION

Since primary malignant bone tumors are rare, there is a dearth of strong evidence based on randomized controlled trials, and recommendations cannot be applied to all the patients. In clinical practice, appropriate treatment of patients with primary malignant bone tumors should be based on the histopathological diagnosis and degree of progression of each case, using these guidelines as a reference.

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.

Prognostic nomogram in middle-aged and elderly patients with chordoma: A SEER-based study

BACKGROUND

Chordoma is a bone tumor that tends to occur in middle-aged and elderly people. It grows relatively slowly but is aggressive. The prognosis of middle-aged and elderly patients with chordoma is quite different from that of young patients with chordoma.

OBJECTIVES

The purpose of the research was to construct a nomogram to predict the Individualized prognosis of middle-aged and elderly (age greater than or equal to 40 years) patients with chordoma.

METHODS

In this study, we screened 658 patients diagnosed with chordoma from 1983 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. We determined the independently prognostic factors that affect the survival of patients by univariate and multivariate Cox proportional hazards model. Based on the independent prognostic factors, we constructed a nomogram to predict the overall survival (OS) rates of middle-aged and elderly patients with chordoma at 3 and 5 years. The validation of this nomogram was completed by evaluating the calibration curve and the C-index.

RESULTS

We screened a total of 658 patients and divided them into two cohort. Training cohort had 462 samples and validation cohort had 196 samples. The multivariate Cox proportional hazards model of the training group showed an association of age, tumor size, histology, primary site, surgery, and extent of disease with OS rates. Based on these results, we constructed the corresponding nomogram. The calibration curve and C-index showed the satisfactory ability of the nomogram in terms of predictive ability.

CONCLUSION

Nomogram can be an effective prognostic tool to assess the prognosis of middle-aged and elderly patients with chordoma and can help clinicians in medical decision-making and enable patients to receive more accurate and reasonable treatment.

Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study

OBJECTIVE

To evaluate the efficacy of the CT-based intratumoral, peritumoral, and combined radiomics signatures in predicting progression-free survival (PFS) of patients with chondrosarcoma (CS).

METHODS

In this study, patients diagnosed with CS between January 2009 and January 2022 were retrospectively screened, and 214 patients with CS from two centers were respectively enrolled into the training cohorts (institution 1, n = 113) and test cohorts (institution 2, n = 101). The intratumoral and peritumoral radiomics features were extracted from CT images. The intratumoral, peritumoral, and combined radiomics signatures were constructed respectively, and their radiomics scores (Rad-score) were calculated. The performance of intratumoral, peritumoral, and combined radiomics signatures in PFS prediction in patients with CS was evaluated by C-index, time-dependent area under the receiver operating characteristics curve (time-AUC), and time-dependent C-index (time C-index).

RESULTS

Eleven, 7, and 16 features were used to construct the intratumoral, peritumoral, and combined radiomics signatures, respectively. The combined radiomics signature showed the best prediction ability in the training cohort (C-index, 0.835; 95%; confidence interval [CI], 0.764-0.905) and the test cohort (C-index, 0.800; 95% CI, 0.681-0.920). Time-AUC and time C-index showed that the combined signature outperformed the intratumoral and peritumoral radiomics signatures in the prediction of PFS.

CONCLUSION

The CT-based combined signature incorporating intratumoral and peritumoral radiomics features can predict PFS in patients with CS, which might assist clinicians in selecting individualized surveillance and treatment plans for CS patients.

CRITICAL RELEVANCE STATEMENT

Develop and validate CT-based intratumoral, peritumoral, and combined radiomics signatures to evaluate the efficacy in predicting prognosis of patients with CS.

KEY POINTS

• Reliable prognostic models for preoperative chondrosarcoma are lacking. • Combined radiomics signature incorporating intratumoral and peritumoral features can predict progression-free survival in patients with chondrosarcoma. • Combined radiomics signature may facilitate individualized stratification and management of patients with chondrosarcoma.

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.

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.

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.

Prognostic Factors for Survival in Patients With High-grade Chondrosarcoma

BACKGROUND/AIM

Chondrosarcoma (CS) is a rare primary malignant bone tumor, which is the second most common tumor after osteosarcoma. Since chemotherapy and radiotherapy have poor efficacy for CS, amputation or surgical wide resection is the main strategy for localized high-grade CS, making CS therapy difficult. As studies on high-grade CS are limited owing to its rare nature, there are many unknown prognostic factors for survival.

PATIENTS AND METHODS

This retrospective cohort study included 44 patients with high-grade CS who underwent surgery at a single institution. Overall survival (OS), distant failure-free survival (DFFS), and local failure-free survival (LFFS) were evaluated using the Kaplan-Meier method. Furthermore, we evaluated prognostic factors for survival in patients with high-grade CS using univariate and multivariate analyses.

RESULTS

The 5-year OS, LFFS, and DFFS rates of high-grade CS were 75.9%, 90.8%, and 66.5%, respectively. Univariate analysis revealed that tumor size, tumor grade, and surgical margin were significant prognostic factors for OS and DFFS, and distant metastasis was significantly associated with OS. Furthermore, the multivariate analysis indicated that the presence of local recurrence and distant metastasis was significantly associated with OS.

CONCLUSION

Local recurrence and distant metastasis were significant prognostic factors for high-grade CS.

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.

Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis

Background

Accurate prediction of prognosis is critical for therapeutic decisions in chondrosarcoma patients. Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. Compared with simplifying the prediction as a binary classification problem, modeling the probability of an event as a function of time by combining it with deep learning can provide better accuracy and flexibility.

Materials and methods

Patients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. Three algorithms—two based on neural networks (DeepSurv, neural multi-task logistic regression [NMTLR]) and one on ensemble learning (random survival forest [RSF])—were selected for training. Meanwhile, a multivariate Cox proportional hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into training and testing datasets at a ratio of 7:3. Hyperparameter tuning was conducted through a 1000-repeated random search with 5-fold cross-validation on the training dataset. The model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-, 3-, 5- and 10-year survival was evaluated using receiver operating characteristic curves (ROC), calibration curves, and the area under the ROC curves (AUC).

Results

A total of 3145 patients were finally enrolled in our study. The mean age at diagnosis was 52 ± 18 years, 1662 of the 3145 patients were male (53%), and mean survival time was 83 ± 67 months. Two deep learning models outperformed the RSF and classical CoxPH models, with the C-index on test datasets achieving values of 0.832 (DeepSurv) and 0.821 (NMTLR). The DeepSurv model produced better accuracy and calibrated survival estimates in predicting 1-, 3- 5- and 10-year survival (AUC:0.895-0.937). We deployed the DeepSurv model as a web application for use in clinical practice; it can be accessed through https://share.streamlit.io/whuh-ml/chondrosarcoma/Predict/app.py.

Conclusions

Time-to-event prediction models based on deep learning algorithms are successful in predicting chondrosarcoma prognosis, with DeepSurv producing the best discriminative performance and calibration.

A new prognostic nomogram for heterotopic ossification formation after elbow trauma : the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction (STEHOP) model

AIMS

Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries.

METHODS

This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation.

RESULTS

Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes.

CONCLUSION

The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963-971.

Clinical Characteristics, Prognostic Factor and a Novel Dynamic Prediction Model for Overall Survival of Elderly Patients With Chondrosarcoma: A Population-Based Study

Background

Chondrosarcoma is the most common primary bone sarcoma among elderly population. This study aims to explore independent prognostic factors and develop prediction model in elderly patients with CHS.

Methods

This study retrospectively analyzed the clinical data of elderly patients diagnosed as CHS between 2004 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. We randomly divided enrolled patients into training and validation group, univariate and multivariate Cox regression analyses were used to determine independent prognostic factors. Based on the identified variables, the nomogram was developed and verified to predict the 12-, 24-, and 36-month overall survival (OS) of elderly patients with CHS. A k-fold cross-validation method (k=10) was performed to validate the newly proposed model. The discrimination, calibration and clinical utility of the nomogram were assessed using the Harrells concordance index (C-index), receiver operating characteristic (ROC) curve and the area under the curve (AUC), calibration curve, decision curve analysis (DCA), the integrated discrimination improvement (IDI) and net reclassification index (NRI). Furthermore, a web-based survival calculator was developed based on the nomogram.

Results

The study finally included 595 elderly patients with CHS and randomized them into the training group (419 cases) and validation group (176 cases) at a ratio of 7:3. Age, sex, grade, histology, M stage, surgery and tumor size were identified as independent prognostic factors of this population. The novel nomogram displayed excellent predictive performance, which can be accessible by https://nomoresearch.shinyapps.io/elderlywithCHS/, with a C-index of 0.800 for the training group and 0.789 for the validation group. The value AUC values at 12-, 24-, and 36-month of 0.866, 0.855, and 0.860 in the training group and of 0.839, 0.856, and 0.840 in the validation group, respectively. The calibration curves exhibited good concordance from the predicted survival probabilities to actual observation. The ROC curves, IDI, NRI, and DCA showed the nomogram was superior to the existing AJCC staging system.

Conclusion

This study developed a novel web-based nomogram for accurately predicting probabilities of OS in elderly patients with CHS, which will contribute to personalized survival assessment and clinical management for elderly patients with CHS.

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.

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.

Refractory recurrent spinal chondrosarcoma: What is the role of salvage surgery?

BACKGROUND

The spinal chondrosarcoma has high risk of recurrence if the initial surgery is not performed in an en bloc fashion. It remains technically demanding to surgically manage the refractory recurrent spinal chondrosarcoma (RRSC). This study is to assess the clinical features and investigate the prognostic factors for patients with RRSCs.

METHODS

forty-nine patients with RRSCs underwent salvage surgeries in our institution, and the clinical characteristics were collected and recorded by two independent reviewers. Univariate and multivariate analyses were performed to investigate the independent prognostic factors of recurrence-free survival (RFS) and overall survival (OS) for patients with RRSCs.

RESULTS

During the mean follow-up of 31.7 ± 21.04 months (Range 9-93), the 3-year RFS and OS rate was 24.5% and 34.5%, respectively. According to the Cox proportional hazards regression model, wide excision with tumor-free margin (>4 mm) was associated with both better RFS and OS for patients with RRSCs. Meanwhile, the number of recurrences ≤2 was beneficial to RFS, while high pathological grade was correlated with worse OS.

CONCLUSIONS

Wide excision with tumor-free margin (>4 mm) is recommendable if appropriate in the salvage surgery for patients with RRSCs. Patients with number of recurrences ≤ 2 and lower pathological grade may have better RFS and OS, respectively.

A Prognostic Nomogram for Predicting Overall Survival in Pediatric Wilms Tumor Based on an Autophagy-related Gene Signature

BACKGROUND

Wilms tumor (WT) is the most common primary renal malignancy in children. Autophagy plays dual roles in the promotion and suppression of various cancers.

OBJECTIVE

The goal of our study was to develop a novel autophagy-related gene (ARG) prognostic nomogram for WT.

METHODS

The Cancer Genome Atlas (TCGA) database was used. We screened the expression profiles of ARGs in 136 WT patients. The differentially expressed prognostic ARGs were evaluated by multivariate Cox regression analysis and survival analysis. A novel prognostic nomogram based on the ARGs and clinical characteristics was established using multivariate Cox regression analysis.

RESULTS

First, 69 differentially expressed ARGs were identified in WT patients. Then, multivariate Cox regression analysis was used to determine 4 key prognostic ARGs (CC3CL1, ERBB2, HIF-α and CXCR4) in WT. According to their ARG expression levels, the patients were clustered into high- and low-risk groups. Next, survival analysis indicated that high-risk patients had significantly poorer overall survival than low-risk patients. The results of functional enrichment analysis suggested that autophagy may play a tumor-suppressive role in the initiation of WT. Finally, a prognostic nomogram with a Harrell's concordance index (C-index) of 0.841 was used to predict the survival probability of WT patients by integrating clinical characteristics and the 4-ARG signature. The calibration curve indicated its excellent predictive performance.

CONCLUSION

In summary, the ARG signature could be a promising biomarker for monitoring the outcomes of WT. We established a novel nomogram based on the ARG signature, which accurately predicts the overall survival of WT patients.

M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection

A good prediction model is useful to accurately predict patient prognosis. Tumor-node-metastasis (TNM) staging often cannot accurately predict prognosis when used alone. Some researchers have shown that the infiltration of M2 macrophages in many tumors indicates poor prognosis. This approach has the potential to predict prognosis more accurately when used in combination with TNM staging, but there is less research in gastric cancer. A multivariate analysis demonstrated that CD163 expression, TNM staging, age, and gender were independent risk factors for overall survival. Thus, these parameters were assessed to develop the nomogram in the training data set, which was tested in the validation and whole data sets. The model showed a high degree of discrimination, calibration, and good clinical benefit in the training, validation, and whole data sets. In conclusion, we combined CD163 expression in macrophages, TNM staging, age, and gender to develop a nomogram to predict 3- and 5-year overall survivals after curative resection for gastric cancer. This model has the potential to provide further diagnostic and prognostic value for patients with gastric cancer.

Establishment of a non-squamous cell carcinoma of the larynx nomogram prognostic model and prognosis analysis

OBJECTIVE

We aimed to compare the prognosis of laryngeal squamous cell carcinoma (LSCC) and nSCCs of the larynx.Then we established a nomogram for nSCCs of the larynx.

METHODS

Prognosis between the 529 pairs nSCCs of the larynx patients and LSCC patients were compared after propensity score matching (PSM). 591 nSCCs of the larynx patients were divided into the modeling and validation groups. Univariate and multivariate Cox analyses obtain independent prognostic factors, which were then included in the nomogram to predict the 3 and 5 year survival. Prognostic accuracy of the nomogram was evaluated using the consistency index (C-index) and the calibration curve.

RESULTS

Prognosis of nSCCs of the larynx was poorer than LSCC. Age, race, tumor location, tumor-node-metastasis stage, and method were independent prognostic factors of nSCCs of the larynx (P < 0.05). Internal and external validation proves the nomogram reliable CONCLUSION: The nomogram showed good prognostic accuracy and would assist clinicians in making more accurate evaluations for patients.

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.

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.

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.

Establishing a novel prognostic tool for Ewing sarcoma patients: Surveillance, Epidemiology, and End Results database analysis

Patients diagnosed with Ewing sarcoma (ES) usually experience poor outcomes. Accurate prediction of ES patients' prognosis is essential to improve their survival. Given that ES is a relatively rare tumor with a low incidence, we aim at developing a prognostic nomogram of ES patients based on a large sample analysis.We used the Surveillance, Epidemiology, and End Results (SEER) database to screen eligible patients diagnosed ES of bone. This retrospective study presented the clinicopathological characteristics and prognosis of ES. We randomly assigned all ES patients to 2 sets (training set and validation set) with an equal number of patients. In order to identify independent factors of survival, we performed univariate and multivariate Cox analysis in the training set. Then, we constructed novel nomograms to predict survival of ES patients by integrating significant independent variables from the training set. The prognostic performance of constructed nomograms was examined using concordance index (C-index) and calibration curves in both training and validation set.We included a total of 988 eligible cases diagnosed ES of bone between 2000 and 2015. Age >18 years, distant metastasis, tumor size >10 cm, and no surgery were independent risk factors for poorer survival. Our survival prediction nomograms were established based on those 4 independent risk factors. Good calibration plots were achieved in internal and external validation. The internal validation C-indexes of the nomogram for overall survival (OS) and cancer-specific survival (CSS) were 0.733 and 0.737, respectively. Similar good results were also achieved in external validation setting.The established nomograms show good performance and allow for better evaluating the prognosis of ES patients and recommending appropriate instructions.

Establishment of prognostic nomogram for elderly colorectal cancer patients: a SEER database analysis

Background

This study aimed to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in elderly colorectal cancer (ECRC) patients (Age ≥ 70).

Methods

The clinical variables of patients confirmed as ECRC between 2004 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analysis were performed, followed by the construction of nomograms in OS and CSS.

Results

A total of 44,761 cases were finally included in this study. Both C-index and calibration plots indicated noticeable performance of newly established nomograms. Moreover, nomograms also showed higher outcomes of decision curve analysis (DCA) and the area under the curve (AUC) compared to American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage and SEER stage.

Conclusions

This study established nomograms of elderly colorectal cancer patients with distinct clinical values compared to AJCC TNM and SEER stages regarding both OS and CSS.

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.

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.

Risk Factors for Incidence and Prognosis in Chondrosarcoma Patients with Pulmonary Metastasis at Initial Diagnosis

Background

The incidence and prognostic factors of chondrosarcoma patients have been reported in early studies. However, the association between risk factors and the incidence or prognosis of chondrosarcoma patients with pulmonary metastasis remains unclear. Therefore, we assessed these risk factors among chondrosarcoma patients with pulmonary metastasis.

Material/Methods

From 1365 chondrosarcoma patients in the Surveillance, Epidemiology, and End Results (SEER) database, we collected the information of 69 patients with pulmonary metastasis at the initial diagnosis of chondrosarcoma from 2010 to 2016. We investigated the incidence, risk factors, and prognostic factors for pulmonary metastasis patients by using multivariate logistic regression and multivariate Cox regression analyses.

Results

Data from a total of 69 (6.8%) chondrosarcoma patients with pulmonary metastasis at initial diagnosis were extracted. Patients with the following characteristics were positively associated with higher risk of pulmonary metastasis: dedifferentiated subtype, high grade of malignancy, extracompartmental tumor (Enneking B), presence of regional lymph nodes, local recurrence, large tumor size (larger than 15 cm), and being married. Older patients (older than 67 years), and patients with clear cell chondrosarcoma or large tumor size (larger than 15 cm) exhibited the worse prognosis and survival (overall and cancer-specific). Resection of the primary tumor tended to be correlated with a better prognosis.

Conclusions

The incidence of pulmonary metastasis in chondrosarcoma was approximately 6.8%, with poor prognosis. Identifying risk factors and their associations with the incidence and prognosis in chondrosarcoma patients with pulmonary metastasis could provide a reference for clinical surveillance and guide the design of personalized treatment plans.

Prognostic Factors and Nomograms to Predict Overall and Cancer-Specific Survival for Children with Wilms' Tumor

Objective

This study is aimed at constructing and verifying nomograms that forecast overall survival (OS) and cancer-specific survival (CSS) of children with Wilms' tumor (WT).

Patients and methods

Clinical information of 1613 WT patients who were under 18 years old between 1988 and 2010 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. Using these data, we performed univariate as well as multivariate Cox's regression analyses to determine independent prognostic factors for WT. Then, nomograms to predict 3- and 5-year OS and CSS rates were constructed based on the identified prognostic factors. The nomograms were validated externally and internally. The nomograms' reliability was evaluated utilizing receiver operating characteristic (ROC) curves and concordance indices (C-indices).

Results

1613 WT patients under 18 were involved in the study and randomly divided into the training (n = 1210) and validation (n = 403) cohorts. Age at diagnosis, tumor laterality, tumor size, tumor stage, and use of surgery were determined as independent prognostic factors for OS and CSS in WT and were further applied to construct prognostic nomograms. The C-index and area under the receiver operating characteristic curve (AUC) revealed the great performance of our nomograms. Internal and external calibration plots also showed excellent agreement between actual survival and nomogram prediction.

Conclusion

Precise and convenient nomograms were developed for forecasting OS and CSS of children with WT. These nomograms were able to offer accurate and individualized prognosis and assisted clinicians in performing suitable therapy.

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.

Regional Lymph Node Involvement Is Associated With Poorer Survivorship in Patients With Chondrosarcoma: A SEER Analysis

BACKGROUND

Regional lymph node involvement is thought to be rare in patients with chondrosarcoma, but its actual prevalence is unclear. Additionally, it is often not considered when prognostic factors are analyzed in patients with chondrosarcoma. However, it has been well established that lymph node involvement is a poor prognostic marker in patients with many types of bone and soft tissue sarcoma, including rhabdomyosarcoma, osteosarcoma, and Ewing's sarcoma. Although lymph node metastases are rare among all sarcoma types, it is important to consider whether lymph node metastases should be assessed in patients with chondrosarcoma because these metastases may impact survival.

QUESTIONS/PURPOSES

(1) What is the reported prevalence of regional lymph node involvement in patients with chondrosarcoma? (2) Do patients who have chondrosarcomas with regional lymph node involvement have different clinicopathologic presentations and survival than patients without regional lymph node involvement? (3) Is regional lymph node involvement independently associated with prognosis in patients with chondrosarcoma?

METHODS

The data of patients with chondrosarcoma registered in the Surveillance Epidemiology and End Results database (SEER) (1988-2015) were analyzed for the reported prevalence of regional lymph node involvement and its relationship with clinicopathologic features and the 5-year overall survival rate. From 1988 to 2015, 5528 patients with chondrosarcoma were registered in the SEER database. After screening by the inclusion criterion-chondrosarcoma as the first primary tumor, diagnosis with histology confirmation, patients with active followup and available information about regional node status-3374 patients met the inclusion criteria and were analyzed. Demographics and clinicopathologic data were compared using chi-square or Fisher's exact tests. Logistic regression analysis was used to assess the adjusted odds ratio. The overall survival rate was estimated with Kaplan-Meier curves and log-rank tests. Univariate and multivariate analyses of overall survival were performed with Cox proportional hazard models. In addition, a series of sensitivity analyses were performed to assess the robustness of the final Cox proportional hazard model.

RESULTS

Forty-four patients (1.3%) were recorded in the database as having regional lymph node involvement at the time of the primary diagnosis. Lymph node metastases were more likely to be reported in an extraskeletal primary site (3% [13 of 426] versus 1% [31 of 2948], adjusted odds ratio [OR] = 2.9, 95% CI, 1.5-5.8; p = 0.003) for bone primary sites and tumors with maximum diameter ≥ 8 cm (2% [26 of 1045] versus 1% [10 of 1075], adjusted OR = 2.9, 95% CI, 1.3-6.3; p = 0.008) and poorer differentiation (4% [24 of 608] versus 1% [14 of 2308], adjusted OR = 4.0, 95% CI, 2.0-8.2; p < 0.001), and in those with distant metastases (7% [14 of 203] versus 1% [30 of 3148], adjusted OR = 3.5, 95% CI, 1.7-7.1, p = 0.001). The 5-year overall survival rates of patients with and without regional lymph node involvement were 28% (95% CI, 15-42%) and 77% (95% CI, 75-78%), respectively (p < 0.001). After controlling for age, sex, race, grade, metastatic status, size, and histologic subtype, the presence of regional lymph node involvement was associated with poorer survival (hazard ratio, 2.20; 95% CI, 1.50-3.24; p < 0.001); this finding was confirmed in several sensitivity analyses.

CONCLUSION

The prevalence of regional lymph node involvement in patients with chondrosarcoma was 1.3% in the SEER database. Although chondrosarcomas are rare, patients with chondrosarcomas who have regional node metastases have a poorer prognosis than those who have not reported to have them. This may underrepresent the true proportion of patients with lymph node metastases given the inaccuracies of reporting in this database, but we believe these findings indicate that clinicians should examine patients more carefully for chondrosarcoma with lymph node metastases. Future studies are needed to assess potential treatment strategies to improve the prognosis of these patients.

LEVEL OF EVIDENCE

Level III, prognostic study.

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.

Does the SORG Algorithm Predict 5-year Survival in Patients with Chondrosarcoma? An External Validation

BACKGROUND

We developed a machine learning algorithm to predict the survival of patients with chondrosarcoma. The algorithm demonstrated excellent discrimination and calibration on internal validation in a derivation cohort based on data from the Surveillance, Epidemiology, and End Results (SEER) registry. However, the algorithm has not been validated in an independent external dataset.

QUESTIONS/PURPOSES

Does the Skeletal Oncology Research Group (SORG) algorithm accurately predict 5-year survival in an independent patient population surgically treated for chondrosarcoma?

METHODS

The SORG algorithm was developed using the SEER registry, which contains demographic data, tumor characteristics, treatment, and outcome values; and includes approximately 30% of the cancer patients in the United States. The SEER registry was ideal for creating the derivation cohort, and consequently the SORG algorithm, because of the high number of eligible patients and the availability of most (explanatory) variables of interest. Between 1992 to 2013, 326 patients were treated surgically for extracranial chondrosarcoma of the bone at two tertiary care referral centers. Of those, 179 were accounted for at a minimum of 5 years after diagnosis in a clinical note at one of the two institutions, unless they died earlier, and were included in the validation cohort. In all, 147 (45%) did not meet the minimum 5 years of followup at the institution and were not included in the validation of the SORG algorithm. The outcome (survival at 5 years) was checked for all 326 patients in the Social Security death index and were included in the supplemental validation cohort, to also ascertain validity for patients with less than 5 years of institutional followup. Variables used in the SORG algorithm to predict 5-year survival including sex, age, histologic subtype, tumor grade, tumor size, tumor extension, and tumor location were collected manually from medical records. The tumor characteristics were collected from the postoperative musculoskeletal pathology report. Predicted probabilities of 5-year survival were calculated for each patient in the validation cohort using the SORG algorithm, followed by an assessment of performance using the same metrics as used for internal validation, namely: discrimination, calibration, and overall performance. Discrimination was calculated using the concordance statistic (or the area under the Receiver Operating Characteristic (ROC) curve) to determine how well the algorithm discriminates between the outcome, which ranges from 0.5 (no better than a coin-toss) to 1.0 (perfect discrimination). Calibration was assessed using the calibration slope and intercept from a calibration plot to measure the agreement between predicted and observed outcomes. A perfect calibration plot should show a 45° upwards line. Overall performance was determined using the Brier score, ranging from 0 (excellent prediction) to 1 (worst prediction). The Brier score was compared with the null-model Brier score, which showed the performance of a model that ignored all the covariates. A Brier score lower than the null model Brier score indicated greater performance of the algorithm. For the external validation an F1-score was added to measure the overall accuracy of the algorithm, which ranges between 0 (total failure of an algorithm) and 1 (perfect algorithm).The 5-year survival was lower in the validation cohort than it was in the derivation cohort from SEER (61.5% [110 of 179] versus 76% [1131 of 1544] ; p < 0.001). This difference was driven by higher proportion of dedifferentiated chondrosarcoma in the institutional population than in the derivation cohort (27% [49 of 179] versus 9% [131 of 1544]; p < 0.001). Patients in the validation cohort also had larger tumor sizes, higher grades, and nonextremity tumor locations than did those in the derivation cohort. These differences between the study groups emphasize that the external validation is performed not only in a different patient cohort, but also in terms of disease characteristics. Five-year survival was not different for both patient groups between subpopulations of patients with conventional chondrosarcomas and those with dedifferentiated chondrosarcomas.

RESULTS

The concordance statistic for the validation cohort was 0.87 (95% CI, 0.80-0.91). Evaluation of the algorithm's calibration in the institutional population resulted in a calibration slope of 0.97 (95% CI, 0.68-1.3) and calibration intercept of -0.58 (95% CI, -0.20 to -0.97). Finally, on overall performance, the algorithm had a Brier score of 0.152 compared with a null-model Brier score of 0.237 for a high level of overall performance. The F1-score was 0.836. For the supplementary validation in the total of 326 patients, the SORG algorithm had a validation of 0.89 (95% CI, 0.85-0.93). The calibration slope was 1.13 (95% CI, 0.87-1.39) and the calibration intercept was -0.26 (95% CI, -0.57 to 0.06). The Brier score was 0.11, with a null-model Brier score of 0.19. The F1-score was 0.901.

CONCLUSIONS

On external validation, the SORG algorithm retained good discriminative ability and overall performance but overestimated 5-year survival in patients surgically treated for chondrosarcoma. This internet-based tool can help guide patient counseling and shared decision making.

LEVEL OF EVIDENCE

Level III, prognostic study.

Characteristics and prognosis of pelvic Ewing sarcoma: a SEER population-based study

Background

The pelvis is one of the primary sites of Ewing sarcoma (ES) and is associated with poorer prognoses than the extremities. Due to the rarity of this disease and limited data available, the prognostic factors of pelvic ES remain controversial. Thus, this study aimed to identify independent prognostic factors, and develop a nomogram for predicting survival rates in patients with pelvic ES.

Methods

Using data provided by the Surveillance, Epidemiology, and End Results (SEER) database, variables including age, sex, race, tumor size, tumor stage, surgery, and radiotherapy were analyzed using the Kaplan–Meier method and Cox proportional hazards regression. Based on the results of multivariate analyses, a nomogram was built to predict the overall survival (OS) of patients with pelvic ES. The performance of the nomogram was evaluated by the concordance index (C-index).

Results

A total of 267 cases diagnosed between 2004 and 2016 were included in the study. Univariate and multivariate analyses showed that patients who were younger, white, had a localized tumor stage, or underwent surgery were associated with improved prognoses, while no significant differences were observed in OS based on sex, tumor size, or radiotherapy. A nomogram was developed and the C-index was 0.728, indicating adequate performance for survival prediction.

Conclusions

Age, race, tumor stage, and surgery were identified as independent prognostic factors for the OS of pelvic ES. The nomogram developed in this study can individually predict 3- and 5-year OS in patients with pelvic ES.

Application of nomograms in the prediction of overall survival and cancer-specific survival in patients with T1 high-grade bladder cancer

To predict survival outcomes for individual patients with clinical T1 high-grade (T1HG) bladder cancer (BC), data from the Surveillance Epidemiology and End Results (SEER) database were analyzed in the present study. The data of 6,980 cases of T1HG BC between 2004 and 2014 were obtained from the SEER database. Uni- and multivariate Cox analyses were performed to identify significant prognostic factors. Subsequently, prognostic nomograms for predicting 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates were constructed based on the SEER database. Clinical information from the SEER database was divided into internal and external groups and used to validate the nomograms. In addition, calibration plot diagrams and concordance indices (C-indices) were used to verify the predictive performance of the nomogram. A total of 6,980 patients were randomly allocated to the training cohort (n=4,886) or the validation cohort (n=2094). Univariate and multivariate Cox analyses indicated that age, ethnicity, tumor size, marital status, radiation and surgical status were independent prognostic factors. These characteristics were used to establish nomograms. The C-indices for OS and CSS rate predictions for the training cohort were 0.707 (95% CI, 0.693–0.721) and 0.700 (95% CI, 0.679–0.721), respectively. Internal and external calibration plot diagrams exhibited an excellent consistency between actual survival rates and nomogram predictions, particularly for 3- and 5-year OS and CSS. The significant prognostic factors in patients with T1HG BC were age, ethnicity, marital status, tumor size, status of surgery and use of radiation. In the present study, a nomogram was developed that may serve as an effective and convenient evaluation tool to help surgeons perform individualized survival evaluations and mortality risk determination for patients with T1HG BC.

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.

Application of nomograms to predict overall and cancer-specific survival in patients with chordoma

Background

The survival prediction of patients with chordoma is difficult to make due to the rarity of this oncologic disease. Our objective was to apply a nomogram to predict survival outcomes in individuals with chordoma of the skull base, vertebral column, and pelvis.

Methods

A total of 558 patients with chordoma between 1973 and 2014 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors in patients with chordoma were identified via univariate and multivariate Cox analysis. Then these prognostic factors were incorporated into a nomogram to predict 3- and 5-year overall survival and cancer-specific survival rates. Internal and external data were used to validate the nomograms. Concordance indices (C-indices) were used to estimate the accuracy of this nomogram system.

Results

A total of 558 patients were randomly assigned into a training cohort (n = 372) and a validation cohort (n = 186). Age, surgical stage, tumor size, histology, primary site, and use of surgery were identified as independent prognostic factors via univariate and multivariate Cox analysis (all p < 0.05) and further included to establish the nomogram. The C-indices for overall survival and cancer-specific survival prediction of the training cohort were 0.775 (95% confidence interval, 0.770-0.779) and 0.756 (95% confidence interval, 0.749 -0.762). The calibration plots both showed an excellent consistency between actual survival and nomogram prediction.

Conclusion

Nomograms were constructed to predict overall survival and cancer-specific survival for patients with chordoma of the skull base, vertebral column, and pelvis. The nomogram could help surgeons to identify high risk of mortality and evaluate prognosis in patients with chordoma.

Can clinical radiomics nomogram based on 3D multiparametric MRI features and clinical characteristics estimate early recurrence of pelvic chondrosarcoma?

BACKGROUND

Chondrosarcoma (CS) is the second most common primary malignant bone tumor, with a relatively high recurrence rate. However, an effective method that estimates whether pelvic CS will recur after surgery, which influences the formulation of a clinical treatment plan, remains lacking.

PURPOSE

To develop and validate a clinical radiomics nomograms based on 3D multiparametric magnetic resonance imaging (mpMRI) features and clinical characteristics that could estimate early recurrence (ER) (≤1 year) of pelvic CS.

STUDY TYPE

Retrospective.

POPULATION

In all, 103 patients (ER = 41, non-ER = 62) with histologically proven CS were retrospectively analyzed and divided into a training set (n = 72) and a validation set (n = 31).

FIELD STRENGTH/SEQUENCE

3.0T axial T₁ -weighted (T₁ -w), T₂ -weighted (T₂ -w), diffusion weighted imaging (DWI), contrast-enhanced T₁ -weighted (CET₁ -w).

ASSESSMENT

Risk factors (sex, age, type, grade, resection margins, etc.) associated with ER were evaluated. Five individual models based on T₁ -w, T₂ -w, DWI, CET₁ -w, and clinical data were built. Then we compared the performance of models based on T₁ -w, T₂ -w, CET₁ -w and their combination. Lastly, two nomograms based on the best model + clinical data and DWI + clinical data were built.

STATISTICAL TESTS

The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.

RESULTS

Grade was the most important univariate clinical predictor of ER of pelvic CS patients (odds ratio [OR]1 = 4.616, OR2 = 8.939, P < 0.05). T₁ -w + T₂ -w + CET₁ -w had a significantly higher performance than CET₁ -w in the training set (P = 0.01). Radiomics features are more important than clinical characteristics in clinical radiomics nomograms, especially for multisequence combined features (OR = 3.208, P < 0.01). Clinical radiomics nomogram based on combined features (T₁ -w + T₂ -w + CET₁ -w) + clinical data achieved an AUC of 0.891 and ACC of 0.857, followed by DWI + clinical data (AUC = 0.882, ACC = 0.760) in the validation set.

DATA CONCLUSION

The clinical radiomics nomogram had good performance in estimating ER of pelvic CS patients, which would be helpful in clinical decision-making.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:435-445.

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.

Clinical significance of traditional clinical parameters and inflammatory biomarkers for the prognosis of patients with spinal chondrosarcoma: a retrospective study of 150 patients in a single center

BACKGROUND

To investigate the clinical significance of five inflammatory biomarkers and conventional clinical parameters in prognostic prediction of spinal chondrosarcoma.

METHODS

Univariate and multivariate analyses were performed to investigate independent prognostic factors for recurrence and death of patients with spinal chondrosarcoma. Disease-free survival (DFS) and overall survival (OS) were estimated by Kaplan-Meier curve, and differences were analyzed by log-rank test. The optimal cutoff values for NLR, PLR, LMR, and CAR were determined by X-tile program.

RESULTS

The optimal cutoff value for NLR, PLR, LMR, AGR, and CAR was 2.7, 200, 3.0, 1.5, and 0.2, respectively. Of the 150 patients included, recurrence was detected in 105 patients, and death occurred in 78 patients. Multivariate analysis indicated that Tomita I-III, total resection, and CAR < 0.2 were significantly associated with longer DFS. Meanwhile, preoperative Frankel score D-E, total resection, and CAR < 0.2 were favorable prognostic factors for OS. Subtype analysis showed that only total resection was an independent prognostic factor for DFS of recurrent spinal chondrosarcoma.

CONCLUSION

Total resection could significantly reduce the recurrence rate of spinal chondrosarcoma and improve OS of chondrosarcoma patients. Tomita classification I-III was a favorable factor for DFS, and preoperative Frankel score A-C was an adverse prognostic factor for OS. CAR was the most robust prognostic indicator with a discriminatory ability as compared with other inflammatory indicators. These slides can be retrieved under Electronic Supplementary Material.

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.

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

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

To develop and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) of spinal chondrosarcoma patients.

SUMMARY OF BACKGROUND DATA

In this era of personalized medicine, data those are available to predict the survival of spinal chondrosarcoma patients are still limited due to the rarity of the disease. Nomogram, which has been widely used in clinical oncology, could conveniently and precisely predict survival outcome for individual patient.

METHODS

We retrospectively collected 450 spinal chondrosarcoma patients from the Surveillance, Epidemiology, and End Results (SEER) database between 1984 and 2013. Univariate log-rank and multivariate Cox analyses were used to identify independent prognostic factors. These prognostic factors were included in the nomograms, which predict 3- and 5-year OS and CSS rate. The nomograms were bootstrap validated internally and externally.

RESULTS

A total of 450 patients were collected and randomly assigned into the training (n = 225) and validation (n = 225) cohorts. Age, histologic subtype, grade, tumor size, stage, and surgery were identified as independent prognostic factors for OS and CSS (all P < 0.05) and were further incorporated to construct the nomograms. The concordance indices (C-indices) for internal validation of OS and CSS prediction were 0.807 and 0.821, while for external validation of OS and CSS prediction were 0.756 and 0.767. Internal and external calibration plots both revealed an excellent agreement between nomogram prediction and actual survival.

CONCLUSION

Nomograms were developed to predict OS and CSS for spinal chondrosarcoma patients. The nomograms could assist clinicians in making more accurate survival evaluation and identifying patients with high risk of mortality.

LEVEL OF EVIDENCE

4.

Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

BACKGROUND

Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artificial intelligence that enables computers to learn from data. Studies using machine learning are potentially appealing, because of its possibility to explore complex patterns in data and to improve its models over time.

QUESTIONS/PURPOSES

The purposes of this study were (1) to develop machine-learning algorithms for the prediction of 5-year survival in patients with chondrosarcoma; and (2) to deploy the best algorithm as an accessible web-based app for clinical use.

METHODS

All patients with a microscopically confirmed diagnosis of conventional or dedifferentiated chondrosarcoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2010. SEER covers approximately 30% of the US population and consists of demographic, tumor characteristic, treatment, and outcome data. In total, 1554 patients met the inclusion criteria. Mean age at diagnosis was 52 years (SD 17), ranging from 7 to 102 years; 813 of the 1554 patients were men (55%); and mean tumor size was 8 cm (SD 6), ranging from 0.1 cm to 50 cm. Exact size was missing in 340 of 1544 patients (22%), grade in 88 of 1544 (6%), tumor extension in 41 of 1544 (3%), and race in 16 of 1544 (1%). Data for 1-, 3-, 5-, and 10-year overall survival were available for 1533 (99%), 1512 (98%), 1487 (96%), and 977 (63%) patients, respectively. One-year survival was 92%, 3-year survival was 82%, 5-year survival was 76%, and 10-year survival was 54%. Missing data were imputed using the nonparametric missForest method. Boosted decision tree, support vector machine, Bayes point machine, and neural network models were developed for 5-year survival. These models were chosen as a result of their capability of predicting two outcomes based on prior work on machine-learning models for binary classification. The models were assessed by discrimination, calibration, and overall performance. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0 with 1.0 being perfect discrimination and 0.5 that the model is no better than chance at making a prediction. The Brier score measures the squared difference between the predicted probability and the actual outcome. A Brier score of 0 indicates perfect prediction, whereas a Brier score of 1 indicates the poorest prediction. The Brier scores of the models are compared with the null model, which is calculated by assigning each patient a probability equal to the prevalence of the outcome.

RESULTS

Four models for 5-year survival were developed with c-statistics ranging from 0.846 to 0.868 and Brier scores ranging from 0.117 to 0.135 with a null model Brier score of 0.182. The Bayes point machine was incorporated into a freely available web-based application. This application can be accessed through https://sorg-apps.shinyapps.io/chondrosarcoma/.

CONCLUSIONS

Although caution is warranted, because the prediction model has not been validated yet, healthcare providers could use the online prediction tool in daily practice when survival prediction of patients with chondrosarcoma is desired. Future studies should seek to validate the developed prediction model.

LEVEL OF EVIDENCE

Level III, prognostic study.