scholarly journals A competing risk nomogram predicting cause-specific mortality in patients with lung adenosquamous carcinoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiao Wu ◽  
Wenfeng Yu ◽  
R. H. Petersen ◽  
Hongxu Sheng ◽  
Yiqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Model ◽  
Adenosquamous Carcinoma ◽  
Model Performance ◽  
Risk Groups ◽  
High Risk Group ◽  
Competing Risk ◽  
Predictive Tool ◽  
Treatment Protocols ◽  
Specific Mortality

Abstract Background Adenosquamous carcinoma (ASC) is an uncommon histological subtype of lung cancer. The purpose of this study was to assess the cumulative incidences of lung cancer-specific mortality (LC-SM) and other cause-specific mortality (OCSM) in lung ASC patients, and construct a corresponding competing risk nomogram for LC-SM. Methods Data on 2705 patients with first primary lung ASC histologically diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function (CIF) was utilized to calculate the 3-year and 5-year probabilities of LC-SM and OCSM, and a competing risk model was built. Based on the model, we developed a competing risk nomogram to predict the 3-year and 5-year cumulative probabilities of LC-SM and the corresponding concordance indexes (C-indexes) and calibration curves were derived to assess the model performance. To evaluate the clinical usefulness of the nomogram, decision curve analysis (DCA) was conducted. Furthermore, patients were categorized into three groups according to the tertile values of the nomogram-based scores, and their survival differences were assessed using CIF curves. Results The 3-year and 5-year cumulative mortalities were 49.6 and 55.8% for LC-SM and 8.2 and 11.8% for OCSM, respectively. In multivariate analysis, increasing age, male sex, no surgery, and advanced T, N and M stages were related to a significantly higher likelihood of LC-SM. The nomogram showed good calibration, and the 3-year and 5-year C-indexes for predicting the probabilities of LC-SM in the validation cohort were both 0.79, which were almost equal to those of the ten-fold cross validation. DCA demonstrated that using the nomogram gained more benefit when the threshold probabilities were set within the ranges of 0.24–0.89 and 0.25–0.91 for 3-year and 5-year LCSM, respectively. In both the training and validation cohorts, the high-risk group had the highest probabilities of LC-SM, followed by the medium-risk and low-risk groups (both P < 0.0001). Conclusions The competing risk nomogram displayed excellent discrimination and calibration for predicting LC-SM. With the aid of this individualized predictive tool, clinicians can more expediently devise appropriate treatment protocols and follow-up schedules.

scholarly journals A Competing Risk Nomogram Predicting Cause-specific Mortality in Patients with Lung Adenosquamous Carcinoma

2020 ◽  
Author(s):  
Xiao Wu ◽  
Wenfeng Yu ◽  
RH Petersen ◽  
Hongxu Sheng ◽  
Yiqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Model ◽  
Adenosquamous Carcinoma ◽  
Model Performance ◽  
Risk Groups ◽  
High Risk Group ◽  
Competing Risk ◽  
Predictive Tool ◽  
Treatment Protocols ◽  
Specific Mortality

Abstract Background: Adenosquamous carcinoma (ASC) is an uncommon histological subtype of lung cancer. The purpose of this study was to assess the cumulative incidences of lung cancer-specific mortality (LC-SM) and other cause-specific mortality (OCSM) in lung ASC patients, and construct a corresponding competing risk nomogram for LC-SM. Methods: Data on 2,705 patients with first primary lung ASC histologically diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function (CIF) was utilized to calculate the 3-year and 5-year probabilities of LC-SM and OCSM, and a competing risk model was built. Based on the model, we developed a competing risk nomogram to predict the 3-year and 5-year cumulative probabilities of LC-SM and the corresponding concordance indexes (C-indexes) and calibration curves were derived to assess the model performance. To evaluate the clinical usefulness of the nomogram, decision curve analysis (DCA) was conducted. Furthermore, patients were categorized into three groups according to the tertile values of the nomogram-based scores, and their survival differences were assessed using CIF curves. Results: The 3-year and 5-year cumulative mortalities were 49.6% and 55.8% for LC-SM and 8.2% and 11.8% for OCSM, respectively. In multivariate analysis, increasing age, male sex, no surgery, and advanced T, N and M stages were related to a significantly higher likelihood of LC-SM. The nomogram showed good calibration, and the 3-year and 5-year C-indexes for predicting the probabilities of LC-SM in the validation cohort were both 0.79, which were almost equal to those of the ten-fold cross validation. DCA demonstrated that using the nomogram gained more benefit when the threshold probabilities were set within the ranges of 0.24-0.89 and 0.25-0.91 for 3-year and 5-year LCSM, respectively. In both the training and validation cohorts, the high-risk group had the highest probabilities of LC-SM, followed by the medium-risk and low-risk groups (both P<0.0001). Conclusions: The competing risk nomogram displayed excellent discrimination and calibration for predicting LC-SM. With the aid of this individualized predictive tool, clinicians can more expediently devise appropriate treatment protocols and follow-up schedules.

scholarly journals Competing Risk Nomograms Predicting Cause-specific Mortality and Competing Mortality in Patients with Lung Adenosquamous Carcinoma

2020 ◽  
Author(s):  
Xiao Wu ◽  
Wenfeng Yu ◽  
RH Petersen ◽  
Hongxu Sheng ◽  
Yiqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cumulative Incidence ◽  
Risk Model ◽  
Adenosquamous Carcinoma ◽  
Model Performance ◽  
Competing Risk ◽  
Predictive Tool ◽  
Treatment Protocols ◽  
Specific Mortality ◽  
Performance Results

Abstract Background Adenosquamous carcinoma (ASC) is an uncommon histological subtype of lung cancer. The purpose of this study was to assess the cumulative incidence of lung cancer-specific mortality (LC-SM) and other cause-specific mortality (OCSM) for lung ASC patients, and construct the corresponding competing risk nomograms. Methods Data on 2,705 patients with first primary lung ASC histologically diagnosed during 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Cumulative incidence function (CIF) was utilized to calculate the 3-year and 5-year probabilities of LC-SM and OCSM, and a competing risk model was built. Based on the model, we developed the competing risk nomograms, and the corresponding concordance indexes ( C -index) and calibration curves were derived to assess the model performance. Results The 3-year and 5-year cumulative mortalities were 49.6% and 55.8% for LC-SM, and 8.2% and 11.8% for OCSM, respectively. In multivariate analysis, increasing age, male sex, no surgery, and advanced T, N and M stage were related to a significantly higher likelihood of LC-SM, in contrast with increasing age and surgery served as predictors for an elevated risk of OCSM. The nomogram showed a good calibration, and the 5-year C-indexes for predicting probabilities of LC-SM and OCSM in the validation cohort were 0.79 and 0.63, respectively. Conclusions The competing risk nomogram displayed an excellent discrimination and calibration, especially for predicting LC-SM. With the aid of this individualized predictive tool, it is more expedient for clinicians to devise appropriate treatment protocols and follow-up schedules.

scholarly journals Differences in Cancer-Specific Mortality of Right- Versus Left-Sided Colon Adenocarcinoma: A Surveillance, Epidemiology, and End Results Database Analysis

2017 ◽  
pp. 1-9 ◽  
Author(s):  
Chenyang Wang ◽  
Zev A. Wainberg ◽  
Ann Raldow ◽  
Percy Lee
Keyword(s):  
Colon Cancer ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Risk Model ◽  
Statistical Significance ◽  
Competing Risk ◽  
Regional Stage ◽  
Increased Risk ◽  
Specific Mortality ◽  
Cancer Specific Mortality

Purpose Studies have shown an increased risk for all-cause mortality with right-sided colon cancer (RCC) as compared with left-sided colon cancer (LCC). However, these studies were unable to directly account for mortality events unrelated to cancer, known as other-cause mortality. We investigated the difference in cancer-specific mortality (CSM) between RCC and LCC at localized, regional, and metastatic stages, according to the Fine and Gray proportional hazards model, while accounting for other-cause mortality as a competing risk. Methods Using the SEER database, we identified 90,635 patients with LCC (ie, involving the splenic flexure, descending, sigmoid, and rectosigmoid colon) and 112,679 patients with RCC (ie, involving the cecum, ascending, hepatic flexure, and transverse colon) diagnosed from 1998 to 2013. We performed a competing risk analysis for CSM using the Fine and Gray proportional hazard model, adjusting for age, sex, race, tumor grade, surgery status, year of diagnosis, and tumor laterality, with two-sided testing and a statistical significance threshold of 0.05. Results Compared with LCC, RCC demonstrated statistically significant decreased CSM at the localized stage (adjusted hazards ratio [AHR], 0.865; P < .001), equivalent CSM at the regional stage (AHR, 0.990; P = .440), and increased CSM at the metastatic stage (AHR, 1.175; P < .001). Conclusion Using a competing risk model, we have shown that RCC, compared with LCC, is associated with lower CSM at the localized stage, equivalent CSM at the regional stage, and higher CSM at the metastatic stage. This pattern may correlate with variation in genetic factors, including known decreased prevalence of microsatellite instability in RCC with regional and metastatic disease.

scholarly journals An investigative expansion of a competing risk model for first failure site in locally advanced non-small cell lung cancer

2019 ◽  
Vol 58 (10) ◽  
pp. 1386-1392 ◽  
Author(s):  
Thomas Lacoppidan ◽  
Ivan R. Vogelius ◽  
Mette Pøhl ◽  
Malene Strange ◽  
Gitte F. Persson ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Risk Model ◽  
Locally Advanced ◽  
Small Cell ◽  
Competing Risk ◽  
Small Cell Lung ◽  
Competing Risk Model ◽  
Failure Site

Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Iron Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

scholarly journals Testing Mayo Clinic’s New 20/20/20 Risk Model in Another Cohort of Smoldering Myeloma Patients: A Retrospective Study

2021 ◽  
Vol 28 (3) ◽  
pp. 2029-2039
Author(s):  
Camille Tessier ◽  
Thomas Allard ◽  
Jean-Samuel Boudreault ◽  
Rayan Kaedbey ◽  
Vincent Éthier ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Risk Model ◽  
Risk Groups ◽  
High Risk Group ◽  
M Protein ◽  
Clinical Settings ◽  
Increased Risk ◽  
Risk Of Progression ◽  
Bone Marrow Plasma ◽  
Smoldering Myeloma

Background—smoldering multiple myeloma (SMM) risk of progression to multiple myeloma (MM) is highly heterogeneous and several models have been suggested to predict this risk. Lakshman et al. recently proposed a model based on three biomarkers: bone marrow plasma cell (BMPC) percentage > 20%, free light chain ratio (FLCr) > 20 and serum M protein > 20 g/L. The goal of our study was to test this “20/20/20” model in our population and to determine if similar results could be obtained in another cohort of SMM patients. Method—we conducted a retrospective, single center study with 89 patients diagnosed with SMM between January 2008 and December 2019. Results—all three tested biomarkers were associated with an increased risk of progression: BMPC percentage ≥ 20% (hazard ratio [HR]: 4.28 [95%C.I., 1.90–9.61]; p < 0.001), serum M protein ≥ 20 g/L (HR: 4.20 [95%C.I., 1.90–15.53]; p = 0.032) and FLCr ≥ 20 (HR: 3.25 [95%C.I., 1.09–9.71]; p = 0.035). The estimated median time to progression (TTP) was not reached for the low and intermediate risk groups and was 29.1 months (95%C.I., 3.9–54.4) in the high-risk group (p = 0.006). Conclusions—the 20/20/20 risk stratification model adequately predicted progression in our population and is easy to use in various clinical settings.

scholarly journals Survival After Wedge Resection Versus Lobectomy for Stage Ia Second Primary NSCLC with Previous Lung Cancer-Directed Surgery

2020 ◽  
Author(s):  
Congkuan Song ◽  
Zhiquan Wu ◽  
Donghu Yu ◽  
Zixin Guo ◽  
Qingwen Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Model ◽  
Early Stage ◽  
Wedge Resection ◽  
Second Primary ◽  
Survival Difference ◽  
Specific Mortality ◽  
Stage Ia ◽  
Cancer Specific Mortality ◽  
Nsclc Patients

Abstract Background: The surgical procedure for early-stage second primary non-small cell lung cancer (SP-NSCLC) remains controversial, especially for patients with previous lung cancer-directed surgery. This study aims to compare the survival after wedge resection and lobectomy for these patients. Methods: Stage IA SP-NSCLC patients with clear information were searched from the Surveillance, Epidemiology, and End Results (SEER) database. Cox proportional hazard model, competing risk model and Kaplan-Meier survival curve were used to describe the survival difference between wedge resection and lobectomy. A 1:1 propensity score matching (PSM) method was also performed to reduce the potential impact of confounding factors between the two groups. Results: Of the 320 eligible stage IA SP-NSCLC patients included in this study, 238 (74.4%) patients underwent wedge resection and 82 (25.6%) patients received lobectomy. The 5-year overall survival (OS) was 61.3% with wedge resection and was 66.1% with lobectomy. Both before and after PSM, wedge resection showed similar survival rate and lung cancer specific mortality as lobectomy in the entire cohort. Additionally, in all subgroup analyses, wedge resection demonstrated equivalent survival to lobectomy. However, in the female, sub-lobectomy for FPLC and interval ≤ 24 months subgroups, wedge resection had a higher lung cancer specific mortality than lobectomy (fine-gray test, all p < 0.05). Conclusion: In conclusion, wedge resection was comparable to lobectomy in OS with all relevant parameters. In a few cases, lung cancer specific mortality in wedge resection group was higher than that in lobectomy group.

scholarly journals Nomogram to Predict Cause-specific Mortality of Patients with Rectal Adenocarcinoma Undergoing Surgery: A Competing Risk Analysis.

2020 ◽  
Author(s):  
Xu Zhang ◽  
Fengshuo Xu ◽  
Yadi Bin ◽  
Tianjie Liu ◽  
Zhichao Li ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Calibration Curve ◽  
Survival Data ◽  
Risk Model ◽  
Univariate Analysis ◽  
Rectal Adenocarcinoma ◽  
Competing Risk ◽  
Competing Risk Analysis ◽  
Subdistribution Hazard ◽  
Specific Mortality

Abstract Background: Rectal adenocarcinoma is one of major public health problems, severely threatening people’s health and life. Cox proportional hazard models have been applied in previous studies widely to analyze survival data. However, such models ignore competing risks and treat them as censored, resulting in excessive statistical errors. Therefore, a competing-risk model was applied with the aim of decreasing risk of bias and thereby obtaining more-accurate results and establishing a competing-risk nomogram for better guiding clinical practice.Methods: A total of 22,879 rectal adenocarcinoma cases who underwent primary-site surgical resection were collected from the SEER (Surveillance, Epidemiology, and End Results) database. Death due to rectal adenocarcinoma (DRA) and death due to other causes (DOC) were two competing endpoint events in the competing-risk regression analysis. The cumulative incidence function (CIF) for DRA and DOC at each time point was calculated. Gray’s test was applied in the univariate analysis and Gray’s proportional subdistribution hazard model was adopted in the multivariate analysis to recognize significant differences among groups and obtain significant factors that could affect patients’ prognosis. Next, A competing-risk nomogram was established predicting the cause-specific outcome of rectal adenocarcinoma cases. Finally, we plotted calibration curve and calculated concordance indexes (c-index) to evaluate the model performance.Results: 22,879 patients were included finally. The results showed that age, race, marital status, chemotherapy, AJCC stage, tumor size, and number of metastasis lymph nodes were significant prognostic factors for postoperative rectal adenocarcinoma patients. We further successfully constructed a competing-risk nomogram to predict the 1-year, 3-year, and 5-year cause-specific mortality of rectal adenocarcinoma patients. The calibration curve and C-index indicated that the competing-risk nomogram model had satisfactory prognostic ability.Conclusion: Competing-risk analysis could help us obtain more-accurate results for rectal adenocarcinoma patients who had undergone surgery, which could definitely help clinicians obtain accurate prediction of the prognosis of patients and make better clinical decisions.

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