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

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

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

Bilateral primary ovarian Ewing sarcoma recurring as left submandibular lymphadenopathy diagnosed on cytology

Ewing sarcoma (ES) is a highly malignant and aggressive small round-cell tumor originating from primitive neuroepithelium and mesenchymal stem cells. It is usually seen in children and adolescents with a male predilection and a preponderance to occur in long bones. Although skeletal/soft tissue ES is encountered in clinical practice, primary ES of the genital tract, particularly bilateral primary ovarian ES, is highly uncommon, with only a handful of cases reported worldwide. Ovarian ES is occasionally reported to involve para-aortic and pelvic lymph nodes in advanced stages. Still, cervical lymph node metastasis from ovarian ES is an infrequent clinical occurrence and, when present, indicates a worse prognosis. Here, we present an intriguing case of bilateral peripheral primary ovarian ES in an adult female, recurring as metastasis in the left submandibular lymph node. This case underlines the importance of keeping metastasis from ES as a possible differential while diagnosing metastatic small round cell tumors in peripheral lymph nodes. It also highlights the usefulness of a minimally invasive diagnostic modality of fine needle aspiration cytology and cell block preparation with applied ancillary techniques of immunohistochemistry and confirmatory molecular testing by fluorescence in-situ hybridization (FISH), for an accurate and quick diagnosis of such entities. The cytological diagnosis of our patient helped in the prompt and early initiation of chemotherapy without requiring any invasive procedure.

Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database

STUDY DESIGN

Retrospective Cohort Study.

OBJECTIVES

This study aimed to develop survival prediction models for spinal Ewing's sarcoma (EWS) based on machine learning (ML).

METHODS

We extracted the SEER registry's clinical data of EWS diagnosed between 1975 and 2016. Three feature selection methods extracted clinical features. Four ML algorithms (Cox, random survival forest (RSF), CoxBoost, DeepCox) were trained to predict the overall survival (OS) and cancer-specific survival (CSS) of spinal EWS. The concordance index (C-index), integrated Brier score (IBS) and mean area under the curves (AUC) were used to assess the prediction performance of different ML models. The top initial ML models with best performance from each evaluation index (C-index, IBS and mean AUC) were finally stacked to ensemble models which were compared with the traditional TNM stage model by 3-/5-/10-year Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA).

RESULTS

A total of 741 patients with spinal EWS were identified. C-index, IBS and mean AUC for the final ensemble ML model in predicting OS were .693/0.158/0.829 during independent testing, while .719/0.171/0.819 in predicting CSS. The ensemble ML model also achieved an AUC of .705/0.747/0.851 for predicting 3-/5-/10-year OS during independent testing, while .734/0.779/0.830 for predicting 3-/5-/10-year CSS, both of which outperformed the traditional TNM stage. DCA curves also showed the advantages of the ensemble models over the traditional TNM stage.

CONCLUSION

ML was an effective and promising technique in predicting survival of spinal EWS, and the ensemble models were superior to the traditional TNM stage model.

A Novel Clinical Tool to Predict Cancer-specific Survival in Postoperative Patients With Primary Spinal and Pelvic Sarcomas: A Large Population-Based Retrospective Cohort Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

Primary osseous sarcomas originating from the spine and pelvis are rare and usually portend inferior prognoses. Currently, the standard treatment for spinal and pelvic sarcomas is surgical resection, but the poor prognosis limits the benefits to postoperative patients. This study aims to identify the independent prognostic factors of cancer-specific survival (CSS) in postoperative patients with primary spinal and pelvic sarcomas and construct a nomogram for predicting these patients' 3-, 5-, and 10-year CSS probability.

METHODS

A total of 452 patients were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. They were divided into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were used to identify these patients' CSS-related independent prognostic factors. Then, those factors were used to construct a prognostic nomogram for predicting the 3-, 5-, and 10-year CSS probability, whose predictive performance and clinical value were verified by the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was constructed.

RESULTS

Sex, histological type, tumor stage, and tumor grade were identified as CSS-related independent prognostic factors. A nomogram with high predictive performance and good clinical value to predict the 3-, 5-, and 10-year CSS probability was constructed, on which a mortality risk stratification system was constructed based to divide these patients into 3 mortality risk subgroups effectively.

CONCLUSIONS

This study constructed and validated a clinical nomogram to predict CSS in postoperative patients with primary spinal and pelvic sarcomas. It could assist clinicians in classifying these patients into different mortality risk subgroups and realize sarcoma-specific management.

Clinical Features, Risk Factors, and Prediction Nomogram for Primary Spinal Osteosarcoma: A Large-Cohort Retrospective Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

The goal of this study was to determine the clinical characteristics of patients with primary spinal osteosarcoma and to construct a practical clinical prediction model for patients to achieve an accurate prediction of overall survival.

METHODS

This study included 230 patients diagnosed between 2004-2015 from the Surveillance, Epidemiology, and End Results database. Independent risk factors were screened in the training set using Cox regression algorithms, and a prognostic model was developed. Internal and external validation sets were used to test the nomogram model's calibration, discrimination, and clinical utility. A risk classification system based on the nomogram was developed and validated.

RESULTS

Four independent prognostic factors were identified, and based on this a nomogram model was developed for predicting patient prognosis. The C-index of the training set was .737, while that of the validation set was .693. The time-varying area under the curve values was greater than .720 in both cohorts. The calibration curves proved that the prediction model has high prediction accuracy. The decision curve analysis showed that the nomogram is clinically useful. A risk classification system was established, which allows all patients to be divided into two different risk groups.

CONCLUSIONS

A nomogram and risk classification system was developed for patients with primary spinal osteosarcoma to accurately predict overall patient survival and achieve risk stratification of patient mortality. These tools are expected to play an important role in clinical practice, informing clinicians in making decisions.

Pediatric presacral tumors with intraspinal extension: a rare entity with diagnostic challenges

The presacral space is a potential space located between the rectum and the lumbosacral spine. It contains various primitive germ cell types that serve as the origin for a range of tumors. Imaging is crucial in characterizing, assessing the extent of and evaluating the treatment response to these tumors. We report a series of six cases of pediatric presacral tumors with intraspinal extension, including an immature sacrococcygeal teratoma (Altman type II), a malignant sacrococcygeal teratoma (Altman type IV), a neuroblastoma, a rhabdomyosarcoma, a clear cell sarcoma and an Ewing's sarcoma of the ilium. These tumors can be broadly categorized as tumors of germ cell, neuroblastic, mesenchymal and osteogenic origin. Despite overlapping imaging features, a review of the existing literature and careful retrospective observation revealed several distinctive features that aid in the optimal characterization of tumors. These include the tumor's epicenter, the pattern and degree of bone involvement, the status of sacral foramina and neural elements, and internal tumor characteristics such as the presence of fat, calcification, hemorrhage and necrosis.

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

Background

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

Methods

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

Results

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

Conclusion

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Ewing sarcoma: A pictorial review of typical and atypical locations with reference to the updated 2020 WHO classification system

Ewing sarcoma (ES) is a small round cell sarcoma which usually occurs in the paediatric and young adult age groups. This pictorial review briefly discusses and presents the typical skeletal and extraskeletal locations which ES is encountered, including the metadiaphysis of long bones, pelvis, chest wall and paravertebral regions. Five cases of ES in atypical locations are then presented along with an observation of demographic differences between skeletal and extraskeletal ES. These cases include ES arising from the metatarsal, scapula, breast, intracranial soft tissue and the paranasal sinuses. Presenting symptoms, treatment approach and prognosis are highly variable depending on the location; and multimodal imaging is centrepiece in the management process. Management usually includes any combination of chemotherapy, radiotherapy and surgery. The WHO classification system was updated in 2020 to include some of the rare genetic subtypes of ES. One such case of the BCOR subtype is demonstrated in this review and occurs in an atypical location. Whether these subtypes have distinct radiological features or a propensity to occur in atypical locations is an area for further research.

A novel clinical tool to predict cancer-specific survival in patients with primary pelvic sarcomas: A large population-based retrospective cohort study

BACKGROUND

Primary osseous sarcoma of the pelvis is rare and has a particularly sinister outcome. This study aims to identify independent prognostic factors of cancer-specific survival (CSS) in patients with primary pelvic sarcoma (PS) and develop a nomogram to predict 3-, 5-, and 10-year probability of CSS in these patients.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 416 patients with primary PS, who were divided into two groups: a training cohort and a validation cohort. Univariate and multivariate Cox analyses were used to screen independent prognostic factors in patients with primary PS. Based on these independent prognostic factors, a prognostic nomogram was developed to predict 3-, 5-, and 10-year probability of CSS. The nomogram's predictive performance and clinical value were evaluated using the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was developed.

RESULTS

Tumor size, tumor stage, histological type, surgery, and chemotherapy were identified as independent prognostic factors for the CSS of primary PS patients. Based on these factors, a nomogram was created to predict the 3-, 5-, and 10-year probability of CSS in these patients. The calibration curve, ROC, and DCA indicated that the nomogram performed well and was appropriate for clinical use, with 3-, 5-, and 10-year areas under ROC curve all higher than 0.800. Furthermore, the nomogram-based mortality risk stratification system could effectively divide these patients into three risk subgroups.

CONCLUSIONS

The nomogram constructed in this study could accurately predict 3-, 5-, and 10-year probability of CSS in patients with primary PS. Clinicians can use the nomogram to categorize these patients into risk subgroups and provide personalized treatment plans.

Management of Unresectable Localized Pelvic Bone Sarcomas: Current Practice and Future Perspectives

Simple Summary

Some locally advanced pelvic bone tumors are deemed unresectable and, as such, not suitable for curative surgery. In this setting, treatment options are generally limited and not unanimous, with decisions being made on an individual basis after multidisciplinary discussion. Ultimately, and notwithstanding the bright prospects raised by novel therapeutic approaches, treatment should be patient-tailored, weighing a panoply of patient- and tumor-related factors.

Abstract

Bone sarcomas (BS) are rare mesenchymal tumors usually located in the extremities and pelvis. While surgical resection is the cornerstone of curative treatment, some locally advanced tumors are deemed unresectable and hence not suitable for curative intent. This is often true for pelvic sarcoma due to anatomic complexity and proximity to vital structures, making treatment options for these tumors generally limited and not unanimous, with decisions being made on an individual basis after multidisciplinary discussion. Several studies have been published in recent years focusing on innovative treatment options for patients with locally advanced sarcoma not amenable to local surgery. The present article reviews the evidence regarding the treatment of patients with locally advanced and unresectable pelvic BS, with the goal of providing an overview of treatment options for the main BS histologic subtypes involving this anatomic area and exploring future therapeutic perspectives. The management of unresectable localized pelvic BS represents a major challenge and is hampered by the lack of comprehensive and standardized guidelines. As such, the optimal treatment needs to be individually tailored, weighing a panoply of patient- and tumor-related factors. Despite the bright prospects raised by novel therapeutic approaches, the role of each treatment option in the therapeutic armamentarium of these patients requires solid clinical evidence before becoming fully established.

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

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

Proton Therapy for Primary Bone Malignancy of the Pelvic and Lumbar Region - Data From the Prospective Registries ProReg and KiProReg

PURPOSE/OBJECTIVES

Multimodality treatments together with local proton therapy (PT) are commonly used in unresectable primary bone malignancies in order to provide better tumor control rate while maintaining good feasibility. The aim of this study is to provide data on outcome of PT for the challenging cohort of pelvic and lumbar bone tumors.

METHODS AND MATERIALS

This retrospective study includes all patients with primary bone malignancy of the pelvis and lumbar spine receiving PT in our institution between May 2013 and December 2019 enrolled in the prospective registries KiProReg and ProReg collecting information on demographics, treatment, tumor characteristics, toxicities, and outcome.

RESULTS

Eighty-one patients were enrolled with a median age of 19.7 years (1.3-85.8). The median follow-up time was 27.5 months (1.2-83.2). The majority of patients was male (64.2%), ECOG status of 0-1 (75.2%), underwent only biopsy (50.6%), received chemotherapy (69.1%) and was assigned for definite PT (70.4%). The predominant tumor characteristics were as follows: Ewing's sarcoma histology (58%), negative nodal involvement (97.5%) and no metastasis at diagnosis (81.5%). Median maximal diameter of tumor was 8 cm (1.4-20). LC, EFS and OS rate were 76.5, 60, and 88.1% at two years and 72.9, 45.7, and 68.9% at three years, respectively. Age over 20 years was a significant negative factor for LC, EFS, and OS. Metastatic disease at initial diagnosis affected OS and ECOG status of 2-4 affected EFS only. Regarding 17 relapsed cases (21%), isolated distant relapse was the most common failure (46.9%) followed by local failure (40.6%). Eleven out of 14 evaluable patients relapsed within high-dose region of radiotherapy. Acute grade 3-4 toxicity was found in 41 patients (50.6%) and all toxicities were manageable. Late grade 3 toxicity was reported in 7 patients (10.4%) without any of grade 4. Most common higher grade acute and late side effects concerned hematologic and musculoskeletal toxicity.

CONCLUSION

Proton therapy resulted in good oncological outcomes when being part of the multimodality treatment for pelvic and lumbar primary bone malignancies. However, distant metastases and local failures within the high-dose region of radiotherapy are still a common issue. Acute and late toxicities of combined therapy were acceptable.

Using machine learning techniques predicts prognosis of patients with Ewing sarcoma

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

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

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

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

SUMMARY OF BACKGROUND DATA

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

METHODS

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

RESULTS

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

CONCLUSION

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

Reconstruction after Subtotal Sacrectomy for Sacral Ewing's Sarcoma Using Tibial Allograft Strut Grafting: A Case Report

We present the case of a 15-year-old girl. Two months after becoming aware of pain, she was diagnosed with a sacral tumor and referred to our department. She was diagnosed with a sacral Ewing's sarcoma; after chemotherapy, it was determined that the tumor could be resected, so surgical treatment was performed. The sacrum and ilium were partially resected at the lower end of S1, and the lumbar vertebrae and pelvis were fixed with a pedicle screw and two iliac screws on each side of L3, and the sacral resection was reconstructed with a tibial strut allograft. No tumor recurrence or metastasis has been observed 1 year postoperatively. She developed bladder and rectal dysfunction, but she remained independent in activities of daily living and her daily life was not limited. The bone fusion in the reconstructed area confirmed the lack of instrumentation looseness. Surgical treatment for sacral Ewing's sarcoma was performed to cure the patient. We believe that the tibial allograft contributed to the patient's ability to walk on her own due to its high mechanical stability. Postoperative bone healing was observed with the same material, suggesting that the tibial allograft is useful for similar procedures.

Dextrose Prolotherapy to Treat Pain, Improve Activities of Daily Living, and Improve Quality of Life in an Ewing's Sarcoma Patient Following Radiation and Chemotherapy Treatment

Advances in the treatment and survival of Ewing’s sarcoma patients create a need to treat underlying symptoms that limit activities of daily living and quality of life. This case describes the treatment of pain in a 25-year-old female pediatric nurse with Ewing’s sarcoma of the pelvis that was in remission following radiation and chemotherapy. She reported medication side effects and limitations in her activities of daily living and quality of life with the chronic use of topical and oral pain medications. A dextrose prolotherapy approach was used to treat her pain, which allowed her to discontinue her pain medication regimen, resulting in an improvement in her activities of daily living and quality of life. The improvement was sustained at the three-year follow-up after the last procedure.

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

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