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Shen W.,Chinese PLA General Hospital | Sakamoto N.,Juntendo University | Yang L.,Epidemiology And Clinical Research Center For Childrens Cancer | Yang L.,Medical Support Center For Japan Environment And Childrens Study
International Journal of Oncology | Year: 2014

The aim of this study was to build a model to predict the survival benefit of radiotherapy for resected rhabdomyosarcoma at the individual level, to help clinicians and their patients make more informed decisions about adjuvant radiotherapy. Patients with resection of rhabdomyosarcoma between 1990 and 2010 were derived from the Surveillance, Epidemiology and End Results database. A multivariate Cox proportional hazard model was built to model cause-specific survival. We used inverse-probability weighting with propensity scores to minimize selection bias in the observation study. The Akaike information criterion technique was used to reduce variables in the model. Nomograms were created with the reduced model after model selection. The study cohort comprised 1578 patients. The 5-year cause-specific survival rate was 64.3% (95% confidence interval (CI) 61.7-66.9%) and the 10-year cause-specific survival rate was 61.4% (95% CI, 58.7-64.2%) for the entire cohort. Five-year cause-specific survival rates were 62.3% (95% CI, 58.6-66.2%) and 66.1% (95% CI, 62.6-69.8%) for patients with surgery alone and adjuvant radiotherapy, respectively (P<0.01). Age, size, histological type, tumor stage, positive regional nodes and adjuvant radiotherapy were retained in the reduced model. Model performance was good, with a c-index of 0.78 (95% CI, 0.76-0.80). This clinical predictive tool can quantify the benefit of adjuvant radiotherapy after resection of rhabdomyosarcoma, and provide patients and clinicians with assistance in treatment selection. Source


Ishida Y.,Pediatric Medical Center | Qiu D.,National Health Research Institute | Maeda M.,Nippon Medical School | Fujimoto J.,Epidemiology And Clinical Research Center For Childrens Cancer | And 16 more authors.
International Journal of Clinical Oncology | Year: 2016

Background: The epidemiology of secondary cancers in childhood cancer survivors has been unknown in Asian countries. Our aim is to assess the incidence and risk factors for secondary cancers through a nationwide survey in Japan. Methods: A retrospective cohort study comprising 10,069 children who were diagnosed with cancer between 1980 and 2009 was conducted in 15 Japanese hospitals. The cumulative incidence rate was calculated using death as the competing risk and compared by the Gray method. The standardized incidence ratio (SIR) was defined as the ratio of the number of observed cancers divided by the number of expected cancers. The risk factors were analyzed using Cox regression analysis. Results: One hundred and twenty-eight patients (1.3 %) developed secondary cancers within a median follow-up of 8.4 years. The cumulative incidence rate was 1.1 % (95 % confidence interval [CI] 0.9–1.4) at 10 years and 2.6 % (95 % CI 2.1–3.3) at 20 years after primary cancer diagnosis. Sensitivity analysis, limited to 5-year survivors (n = 5,387), confirmed these low incidence rates. The SIR of secondary cancers was 12.1 (95 % CI 10.1–14.4). In the Cox analysis, the hazard ratios for secondary cancers were 3.81 (95 % CI 1.53–9.47) for retinoblastoma, 2.78 (95 % CI 1.44–5.38) for bone/soft tissue sarcomas, and 1.81 (95 % CI 1.16–2.83) for allogeneic stem cell transplantation. Conclusions: The cumulative incidence of secondary cancers in children in Japan was not high; however, the SIR was relatively high. Retinoblastoma or sarcoma in addition to allogeneic stem cell transplantation were significant risk factors for secondary cancers. © 2015, Japan Society of Clinical Oncology. Source


Shen W.,Chinese PLA General Hospital | Sakamoto N.,Juntendo University | Yang L.,Epidemiology And Clinical Research Center For Childrens Cancer | Yang L.,Medical Support Center For Japan Environment And Childrens Study
BMC Cancer | Year: 2014

Background: The purpose of this study was to evaluate the survival outcome for middle ear cancer and to construct prognostic models to provide patients and clinicians with more accurate estimates of individual survival probability.Methods: Patients diagnosed with middle ear cancer between 1983 and 2011 were selected for the study from the Surveillance Epidemiology and End Results Program. We used the Kaplan-Meier product limit method to describe overall survival and cause-specific survival. Cox proportional hazards models were fitted to model the relationships between patient characteristics and prognosis. Nomograms for predicting overall survival and cause-specific survival were built using the Cox models established.Results: The entire cohort comprised 247 patients with malignant middle ear cancer. Median duration of follow-up until censoring or death was 25 months (range, 1-319 months). Five-year overall survival and cause-specific survival were 47.4% (95% Confidence Interval (CI), 41.2% to 54.6%) and 58.0% (95% CI, 51.6% to 65.3%), respectively. In multivariable analysis, age, histological subtype, stage, surgery and radiotherapy were predictive of survival. The bootstrap corrected c-index for model predicting overall and cause-specific survival was 0.73 and 0.74, respectively. Calibration plots showed that the predicted survival reasonably approximated observed outcomes.Conclusion: The models represent an objective analysis of all currently available data. The resulting models demonstrated good accuracy in predicting overall survival and cause-specific survival. Nomograms should thus be considered as a useful tool for predicting clinical prognosis. © 2014 Shen et al.; licensee BioMed Central Ltd. Source


Yang L.,Epidemiology And Clinical Research Center For Childrens Cancer | Yang L.,Medical Support Center For Japan Environment And Childrens Study | Takimoto T.,Epidemiology And Clinical Research Center For Childrens Cancer | Fujimoto J.,Epidemiology And Clinical Research Center For Childrens Cancer
BMC Cancer | Year: 2014

Background: The purpose of this study was to develop a prognostic model for the survival of pediatric patients with rhabdomyosarcoma (RMS) using parameters that are measured during routine clinical management. Methods: Demographic and clinical variables were evaluated in 1679 pediatric patients with RMS registered in the Surveillance, Epidemiology, and End Results (SEER) program from 1990 to 2010. A multivariate Cox proportional hazards model was developed to predict median, 5-year and 10-year overall survival (OS). The Akaike information criterion technique was used for model selection. A nomogram was constructed using the reduced model after model selection, and was internally validated. Results: Of the total 1679 patients, 543 died. The 5-year OS rate was 64.5% (95% confidence interval (CI), 62.1-67.1%) and the 10-year OS was 61.8% (95%CI, 59.2-64.5%) for the entire cohort. Multivariate analysis identified age at diagnosis, tumor size, histological type, tumor stage, surgery and radiotherapy as significantly associated with survival (p < 0.05). The bootstrap-corrected c-index for the model was 0.74. The calibration curve suggested that the model was well calibrated for all predictions. Conclusions: This study provided an objective analysis of all currently available data for pediatric RMS from the SEER cancer registry. A nomogram based on parameters that are measured on a routine basis was developed. The nomogram can be used to predict 5- and 10-year OS with reasonable accuracy. This information will be useful for estimating prognosis and in guiding treatment selection. © 2014 Yang et al.; licensee BioMed Central Ltd. Source


Shen W.,Chinese PLA General Hospital | Sakamoto N.,Juntendo University | Yang L.,Epidemiology And Clinical Research Center For Childrens Cancer | Yang L.,Medical Support Center For Japan Environment And Childrens Study Jecs
Annals of Surgical Oncology | Year: 2015

Background: The objective of this study was to estimate probabilities of cancer-specific death and competing death for patients with head and neck squamous cell carcinoma (HNSCC). In addition, we attempted to construct competing risk nomograms to predict prognosis for patients with HNSCC using a large population-based cohort.Methods: Patients diagnosed with nonmetastatic HNSCC between 2000 and 2010 were identified from the Surveillance Epidemiology and End Results Program to form the analytic cohort. We estimated cumulative incident function (CIF) of cancer-specific mortality and competing mortality. Nomograms for predicting probability of death were built with proportional subdistribution hazard models.Results: The study cohort included 23,494 patients with HNSCC. The 5-year CIF for cancer-specific death and competing death were 26.7 % (95 % confidence interval [CI] 26–27.3 %) and 12.7 % (95 % CI 12.2–13.3 %), respectively; 10-year CIF were 32.8 % (95 % CI 31.9–33.6 %) and 23 % (95 % CI 22.1–24 %), respectively. On multivariate analysis, increasing cause-specific mortality was associated with increasing age, increasing tumor size, black race, single status, advanced T and N classifications, and high tumor grade. Increasing probability of competing mortality had a relationship with increasing age, male, black race, single status and nonradiotherapy. Models showed good accuracy with c-index of 0.73 for cause-specific mortality model and 0.69 for competing mortality model.Conclusions: We constructed competing risk nomograms for HNSCC using population-based data. The model used for building nomograms represented good performance. These nomograms can serve to guide management of patients with HNSCC. © 2014, Society of Surgical Oncology. Source

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