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Construction and validation of nomograms for non-metastatic Ewing sarcoma: A prognostic factor analysis based on the SEER database.

ABSTRACT: Ewing sarcoma is the second most common osseous disease in children and adolescents. It presents with a poor prognosis due to the high degree of malignancy and distant metastasis. In order to predict the disease prognosis and investigate a suitable therapeutic strategy for Ewing sarcoma, the present study aimed to describe the clinical characteristics, and to construct and validate nomograms for patients with non-metastatic Ewing sarcoma. A total of 627 cases of non-metastatic Ewing sarcoma were retrospectively collected from the Surveillance, Epidemiology, and End Results database between 2005 and 2014. Survival analysis and a machine learning model were used to identify independent prognostic variables and establish nomograms to estimate overall survival (OS) and cause-specific survival (CSS). The nomograms were bootstrap internally validated and externally validated using non-metastatic Ewing sarcoma cases from the First Affiliated Hospital of Zhengzhou University. The accuracy was also assessed by comparing with current American Joint Committee on Cancer (AJCC) staging systems. The total series consisted of 627 patients with non-metastatic Ewing sarcoma with a mean age of 20.14 years. Age, tumor extension, sex, International Classification of Diseases for Oncology, 3rd Edition histology, surgery and chemotherapy were identified as independent risk factors for OS and CSS. The aforementioned outcomes were incorporated to construct the nomograms, and the concordance indices (C-indices) for internal validation of OS and CSS prediction were 0.791 and 0.813, which were higher than those for AJCC sixth edition (OS, 0.531; CSS, 0.534) and seventh edition (OS, 0.547; CSS, 0.561), while the C-indices for external validation of OS and CSS prediction were 0.834 and 0.825, respectively. In conclusion, age, sex, tumor extension and surgery were independent prognostic factors for both OS and CSS. In addition, with regard to OS, the Ewing sarcoma subtype was a poor factor and chemotherapy was a favorable one. Nomograms based on reduced Cox models attained a satisfactory accuracy in predicting the survival of patients with non-metastatic Ewing sarcoma and could assist clinicians in evaluating survival more accurately.

SUBMITTER: Huang R

PROVIDER: S-EPMC8456482 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMetastatic non-small cell lung cancer (NSCLC) is mostly seen in older patients and is associated with poor prognosis. There is no reliable method to predict the prognosis of elderly patients (≥60 years old) with metastatic NSCLC. The aim of our study was to develop and validate nomograms which accurately predict survival in this group of patients.MethodsNSCLC patients diagnosed between 2010 and 2015 were all identified from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed by significant clinicopathological variables (p < 0.05) selected in multivariate Cox analysis regression.ResultsA total of 9584 patients met the inclusion criteria and were randomly allocated in the training (n = 6712) and validation (n = 2872) cohorts. In training cohort, independent prognostic factors included age, gender, race, grade, tumor site, pathology, T stage, N stage, radiotherapy, surgery, chemotherapy, and metastatic site (p < 0.05) for lung cancer-specific survival (LCSS) and overall survival (OS) were identified by the Cox regression. Nomograms for predicting 1-, 2-, and 3-years LCSS and OS were established and showed excellent predictive performance with a higher C-index than that of the 7th TNM staging system (LCSS: training cohort: 0.712 vs. 0.534; p < 0.001; validation cohort: 0.707 vs. 0.528; p < 0.001; OS: training cohort: 0.713 vs. 0.531; p < 0.001; validation cohort: 0.710 vs. 0.528; p < 0.001). The calibration plots showed good consistency from the predicted to actual survival probabilities both in training cohort and validation cohort. Moreover, the decision curve analysis (DCA) achieved better net clinical benefit compared with TNM staging models.ConclusionsWe established and validated novel nomograms for predicting LCSS and OS in elderly patients with metastatic NSCLC with desirable discrimination and calibration ability. These nomograms could provide personalized risk assessment for these patients and assist in clinical decision.

Project description:Background Myeloid sarcoma (MS) is a rare hematological tumor that presents with extramedullary tumor masses comprising myeloid blasts. A controversial issue is whether MS involving normal hematopoietic sites (liver, spleen, and lymph nodes) should be excluded in future studies. We aimed to compare MS characteristics and outcomes involving hematopoietic and non-hematopoietic sites and construct a prognostic nomogram exclusively for the latter. Methods Data from patients diagnosed with MS between 2000 and 2018 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. According to the primary site, patients were classified as having MS involving hematopoietic sites (hMS) or non-hematopoietic sites (eMS). Clinical characteristics and survival outcomes were compared between the two groups using Wilcoxon, chi-square, and log-rank tests. Cox regression analysis was used to identify eMS prognostic factors to establish prognostic nomograms. The models’ efficiency and value were assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results In total, 694 patients were enrolled, including 86 with hMS and 608 with eMS. There were no sex, race or marital status distribution differences between the two groups. Patients with eMS had better overall and cancer-specific survival rates than those with hMS. Additionally, prognostic factor effects differed between the two groups. Patients with eMS were randomly divided into the training (number of patiens, n=425) and validation cohorts (n=183). Age, first primary tumor, primary site, and chemotherapy were used to establish nomograms. The C-index values of overall survival (OS) and cancer-specific survival (CSS) nomograms were 0.733 (validation: 0.728) and 0.722 (validation: 0.717), respectively. Moreover, ROC, calibration curves, and DCA confirmed our models’ good discrimination and calibration ability and potential clinical utility value. Conclusion Our study described the differences between patients with eMS and those with hMS. Moreover, we developed novel nomograms based on clinical and therapeutic factors to predict patients with eMS’ 1-, 3- and 5-year survival rates.

Project description:IntroductionCancer in patients of childbearing age continues to become increasingly common. The purpose of this study was to explore the impact of metastatic breast cancer (MBC) on overall survival (OS) and cancer-specifific survival (CSS) in patients of childbearing age and to construct prognostic nomograms to predict OS and CSS.MethodsData from MBC patients of childbearing age were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, and the patients were randomly assigned into the training and validation cohorts. Univariate and multivariate Cox analyses were used to search for independent prognostic factors impacting OS and CSS, and these data were used to construct nomograms. The concordance index (C-index), area under the curve (AUC), and calibration curves were used to determine the predictive accuracy and discriminative ability of the nomograms. Additional data were obtained from patients at the Yunnan Cancer Hospital to further verify the accuracy of the nomograms.ResultsA total of 1,700 MBC patients of childbearing age were identifified from the SEER database, and an additional 92 eligible patients were enrolled at the Yunnan Cancer Hospital. Multivariate Cox analyses identifified 10 prognostic factors for OS and CSS that were used to construct the nomograms. The calibration curve for the probabilities of OS and CSS showed good agreement between nomogram prediction and clinical observations. The C-index of the nomogram for OS was 0.735 (95% CI = 0.725-0.744); the AUC at 3 years was 0.806 and 0.794 at 5 years.The nomogram predicted that the C-index of the CSS was 0.740 (95% CI = 0.730- 0.750); the AUC at 3 years was 0.811 and 0.789 at 5 years. The same results were observed in the validation cohort. Kaplan- Meier curves comparing the low-,medium-, and high-risk groups showed strong prediction results for the prognostic nomogram.ConclusionWe identifified several independent prognostic factors and constructed nomograms to predict the OS and CSS for MBC patients of childbearing age.These prognostic models should be considered in clinical practice to individualize treatments for this group of patients.

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Construction and validation of nomograms for non-metastatic Ewing sarcoma: A prognostic factor analysis based on the SEER database.

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