Effect of Postoperative Radiotherapy in Thymoma Patients: A SEER-Based Study

2020 ◽  
pp. 1-7
Author(s):  
Haibo Mou ◽  
Yiyao Kong ◽  
Yingfang Wu ◽  
Ying Wu ◽  
Lanfang Yu
Keyword(s):  
Cox Regression ◽  
Postoperative Radiotherapy ◽  
Population Based ◽  
Conditional Inference ◽  
Postoperative Radiation Therapy ◽  
Cancer Specific Survival ◽  
Prognostic Effect ◽  
Conditional Inference Tree ◽  
Masaoka Stage ◽  
Inference Tree

<b><i>Introduction:</i></b> The role of postoperative radiation therapy (PORT) for thymoma is under debate, especially in patients aged ≥60 years with an advanced stage (Masaoka stages III and IV). We aimed to evaluate the efficacy of PORT for thymoma in a population-based registry. <b><i>Methods:</i></b> A retrospective analysis of the Surveillance, Epidemiology, and End Results (SEER) database was conducted to compare the outcomes of thymoma patients with or without PORT. The primary outcomes were overall survival (OS) and cancer-specific survival (CSS). Conditional inference tree analyses were performed for risk classification according to the study variables. Cox regression was performed to evaluate the prognostic effect of PORT in the specific subgroups. <b><i>Results:</i></b> A total of 2,236 patients were included. The conditional inference tree analysis identified that an age ≥60, a Masaoka stage ≥3, and the year of diagnosis were important factors when classifying patients into prognostic subgroups. PORT was found to be a protective predictor of OS in patients aged ≥60 years, those with a Masaoka stage III—IV, and those diagnosed after 2005. Further subgroup analyses revealed that PORT was significantly associated with a better OS (HR = 0.77) in patients aged ≥60 years, whereas it was not significantly associated with CSS. <b><i>Conclusions:</i></b> An older age (≥60 years) is critical for predicting survival outcomes in thymoma patients. Moreover, patients aged ≥60 years could benefit from PORT in terms of OS.

scholarly journals Effects of marital status on overall and cancer-specific survival in laryngeal cancer patients: a population-based study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhao Ding ◽  
Deshun Yu ◽  
Hefeng Li ◽  
Yueming Ding
Keyword(s):  
Marital Status ◽  
Cancer Patients ◽  
Laryngeal Cancer ◽  
Cox Regression ◽  
Group Analysis ◽  
Population Based ◽  
Cancer Specific Survival ◽  
Cox Regression Analysis ◽  
Prognostic Effect ◽  
The Difference

AbstractMarital status has long been recognized as an important prognostic factor for many cancers, however its’ prognostic effect for patients with laryngeal cancer has not been fully examined. We retrospectively analyzed 8834 laryngeal cancer patients in the Surveillance Epidemiology and End Results database from 2004 to 2010. Patients were divided into four groups: married, widowed, single, and divorced/separated. The difference in overall survival (OS) and cancer-specific survival (CSS) of the various marital subgroups were calculated using the Kaplan–Meier curve. Multivariate Cox regression analysis screened for independent prognostic factors. Propensity score matching (PSM) was also conducted to minimize selection bias. We included 8834 eligible patients (4817 married, 894 widowed, 1732 single and 1391 divorced/separated) with laryngeal cancer. The 5-year OS and CSS of married, widowed, single, and separated/divorced patients were examined. Univariate and multivariate analyses found marital status to be an independent predictor of survival. Subgroup survival analysis showed that the OS and CSS rates in widowed patients were always the lowest in the various American Joint Committee on Cancer stages, irrespective of sex. Widowed patients demonstrated worse OS and CSS in the 1:1 matched group analysis. Among patients with laryngeal cancer, widowed patients represented the highest-risk group, with the lowest OS and CSS.

Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

2021 ◽  
pp. 0044118X2110046
Author(s):  
Veronica Fruiht ◽  
Jordan Boeder ◽  
Thomas Chan
Keyword(s):  
Critical Race Theory ◽  
Parental Education ◽  
Individual Characteristics ◽  
Conditional Inference ◽  
Youth Of Color ◽  
Social Success ◽  
Conditional Inference Tree ◽  
Tree Models ◽  
Nationally Representative ◽  
Inference Tree

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

scholarly journals Impact of pre-existing cardiovascular disease on treatment patterns and survival outcomes in patients with lung cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Atul Batra ◽  
Dropen Sheka ◽  
Shiying Kong ◽  
Winson Y. Cheung
Keyword(s):  
Lung Cancer ◽  
Cardiovascular Disease ◽  
Cox Regression ◽  
Population Based ◽  
Treatment Patterns ◽  
Competing Risk ◽  
Survival Outcomes ◽  
Multistate Model ◽  
Cancer Specific Survival ◽  
Increased Risk

Abstract Background Baseline cardiovascular disease (CVD) can impact the patterns of treatment and hence the outcomes of patients with lung cancer. This study aimed to characterize treatment trends and survival outcomes of patients with pre-existing CVD prior to their diagnosis of lung cancer. Methods We conducted a retrospective, population-based cohort study of patients with lung cancer diagnosed from 2004 to 2015 in a large Canadian province. Multivariable logistic regression and Cox regression models were constructed to determine the associations between CVD and treatment patterns, and its impact on overall (OS) and cancer-specific survival (CSS), respectively. A competing risk multistate model was developed to determine the excess mortality risk of patients with pre-existing CVD. Results A total of 20,689 patients with lung cancer were eligible for the current analysis. Men comprised 55%, and the median age at diagnosis was 70 years. One-third had at least one CVD, with the most common being congestive heart failure in 15% of patients. Pre-existing CVD was associated with a lower likelihood of receiving chemotherapy (odds ratio [OR], 0.53; 95% confidence interval [CI], 0.48–0.58; P < .0001), radiotherapy (OR, 0.76; 95% CI, 0.7–0.82; P < .0001), and surgery (OR, 0.56; 95% CI, 0.44–0.7; P < .0001). Adjusting for measured confounders, the presence of pre-existing CVD predicted for inferior OS (hazard ratio [HR], 1.1; 95% CI, 1.1–1.2; P < .0001) and CSS (HR, 1.1; 95% CI, 1.1–1.1; P < .0001). However, in the competing risk multistate model that adjusted for baseline characteristics, prior CVD was associated with increased risk of non-cancer related death (HR, 1.48; 95% CI, 1.33–1.64; P < 0.0001) but not cancer related death (HR, 0.98; 95% CI, 0.94–1.03; P = 0.460). Conclusions Patients with lung cancer and pre-existing CVD are less likely to receive any modality of cancer treatment and are at a higher risk of non-cancer related deaths. As effective therapies such as immuno-oncology drugs are introduced, early cardio-oncology consultation may optimize management of lung cancer.

scholarly journals Survival predictors of metastatic angiosarcomas: a surveillance, epidemiology, and end results program population-based retrospective study

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Shihong Ren ◽  
Yucheng Wang ◽  
Zhan Wang ◽  
Jinxiang Shao ◽  
Zhaoming Ye
Keyword(s):  
Tumor Size ◽  
Cox Regression ◽  
Radiation Treatment ◽  
Tumor Grade ◽  
Population Based ◽  
Cancer Specific Survival ◽  
Cox Regression Analysis ◽  
Metastatic Sites ◽  
Survival Predictors ◽  
End Results

Abstract Background Angiosarcomas (AS) have poor prognosis and often metastasize to distant sites. The potential predictors of metastatic angiosarcomas (MAS) have not been extensively investigated. The main objective of this study was to identify survival predictors of MAS. Methods Surveillance, Epidemiology, and End Results (SEER) datasets were used to identify patients with MAS from 2010 to 2016. Risk predictors were determined with the aid of Kaplan-Meier and Cox regression model analyses. Results A total of 284 MAS patients met the study entry criteria. Among these, 121 patients (42.6%) were diagnosed with metastasis in bone, 26 in brain (9.2%), 86 in liver (30.3%) and 171 in lung (60.2%). Overall, 96 patients (33.8%) had two or more metastatic sites. The 1- and 3-year overall survival (OS) rates were 20.8 and 3.8% while 1- and 3-year cancer-specific survival (CSS) rates were 22.0 and 5.2%, respectively. Cox regression analysis revealed chemotherapy, radiation treatment (RT) and tumor size ≤10 cm as independent favorable predictors of OS. In terms of CSS, tumor grade IV, tumor size > 10 cm and absence of chemotherapy were independent adverse predictors. Surgery did not prolong survival outcomes (both OS and CSS) in the current cohort. Conclusion MAS is associated with extremely poor survival. Chemotherapy, RT, and tumor size are independent predictors of OS. Chemotherapy and tumor size are independent prognostic factors of CSS. Chemotherapy is therefore recommended as the preferred treatment option for MAS patients.

Natural history and outcomes of synchronous and metachronous colorectal cancers (CRC): A population-based analysis.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 3593-3593
Author(s):  
Jackson Chu ◽  
Ozge Goktepe ◽  
Winson Y. Cheung
Keyword(s):  
Cox Regression ◽  
Large Population ◽  
Population Based ◽  
Survival Outcomes ◽  
Cancer Specific Survival ◽  
Free Survival ◽  
Presenting Symptoms ◽  
Kaplan Meier ◽  
Index Cancer ◽  
Metachronous Tumors

3593 Background: Early data suggest that synchronous and metachronous CRC portend a worse prognosis when compared to solitary CRC. Our aims were to 1) characterize the clinical features and treatment patterns of synchronous and metachronous CRC and 2) compare their survival outcomes with those of solitary CRC. Methods: All patients diagnosed with non-metastatic CRC between 1999 and 2008 and referred to any 1 of 5 regional cancer centers in British Columbia, Canada were reviewed. Synchronous and metachronous CRC were defined as multiple (2 or more) distinct tumors that were diagnosed within and beyond 6 months of the date of index CRC diagnosis, respectively, during the study period. Patients with liver metastases at initial diagnosis were excluded. Kaplan-Meier and Cox regression analyses were used to estimate survival among the different CRC groups. Results: A total of 6360 patients were identified: 6147 (96%) solitary, 178 (3%) synchronous and 35 (1%) metachronous tumors; median age was 68 years (IQR 59-76); 57% were men; and 75% were ECOG 0/1 at the time of index cancer diagnosis. Baseline demographic characteristics were comparable across patients (all p>0.05). Compared with solitary CRC, synchronous and metachronous CRC more commonly affected the colon rather than the rectum (84 vs 85 vs 59%, respectively, p<0.001), but presenting symptoms, treatment approaches, and use of chemotherapy, radiation and surgery were similar among the different tumor groups (all p>0.05). In terms of survival, no differences were observed in 3-year relapse free survival (66 vs 66 vs 56%, p=0.20), 5-year cancer specific survival (69 vs 69 vs 53%, p=0.34) and 5-year overall survival (62 vs 59 vs 49%, p=0.74) for solitary, synchronous and metachronous CRC, respectively. These findings persisted after controlling for known prognostic factors, such as age and ECOG. Conclusions: In this large population-based cohort, there were no differences in survival outcomes among solitary, synchronous and metachronous CRC. Patients who present with multiple tumors in the colon or the rectum should be managed similarly to those who present with an isolated tumor.

Localization value of seizure semiology analyzed by the conditional inference tree method

2015 ◽  
Vol 115 ◽  
pp. 81-87 ◽  
Author(s):  
Dong Wook Kim ◽  
Ki-Young Jung ◽  
Kon Chu ◽  
So-Hee Park ◽  
Seo-Young Lee ◽  
...  
Keyword(s):  
Conditional Inference ◽  
Seizure Semiology ◽  
Conditional Inference Tree ◽  
Tree Method ◽  
Inference Tree

scholarly journals COMPARISON OF THREE MODELS FOR PREDICTING THE SPATIAL DISTRIBUTION OF SOIL ORGANIC CARBON IN BOALEMO REGENCY, SULAWESI

2016 ◽  
Vol 18 (1) ◽  
pp. 42 ◽  
Author(s):  
Eloise Mason ◽  
Yiyi Sulaeman
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Regression Tree ◽  
Digital Soil Mapping ◽  
Conditional Inference ◽  
Conditional Inference Tree ◽  
Soil Landscape ◽  
Inference Tree

<p><em>Information on the spatial distribution of soil organic carbon content is required for sustainable land management. But, creating this map is time consuming and costly. Digital soil mapping methodology make use legacy soil data to create provisional soil organic carbon map. This map helps soil surveyors in allocating next soil observation. This study aimed: (i) to develop predictive statistical soil organic carbon models for Sulawesi, and (ii) to evaluate the best model between the three obtained models. Boalemo Regeny in Gorontalo Province (Sulawesi) was selected as studying area due to abundant legacy soil data. The study covered dataset preparation, model development, and model comparison. Dataset of soil organic carbon at 6 different depths as target was established from 176 soil profiles and 7 terrain parameters were selected as predictors. Soil-landscape models for each soil depth were created using regression tree, conditional inference tree, and multiple linear regression technique.  Result showed that model performance differed among 3 modelling techniques and soil depths. The tree models were better than the multiple linear regression model as they have the lowest RMSE index. The best model in the mountanious area seems to be the regression tree model, whereas in the plains it may be the conditional inference tree. In creating provisional map, several model should be developed and the median of predicted value is used as provisional map.</em></p><p><em> </em></p><p><em>Keywords: Digital soil mapping, multiple linear regression, regression tree, soil-landscape model, soil organic carbon map</em></p>

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