Competing Risk Nomogram Research Articles

Morbidity, Prognostic Factors, and Competing Risk Nomogram for Combined Hepatocellular-Cholangiocarcinoma.

Background Combined hepatocellular-cholangiocarcinoma (CHC) is a rare and heterogeneous histological subtype of primary liver cancer, which is still poorly understood. This study aimed to describe the epidemiological and clinical features, investigate the prognostic indicators, and develop a competing risk nomogram for CHC. Methods The study cohort was taken from the Surveillance, Epidemiology, and End Results database. The annual percent change (APC) in incidence was calculated using the joinpoint regression. The nomogram was developed based on multivariate competing risk survival analyses and validated by calibration curves. Akaike information criterion, Bayesian information criterion, Harrell's C-index, and area under the receiver operating characteristic curves were obtained to compare prognostic performance. Decision curve analysis was introduced to examine the clinical value of the models. Results The overall incidence of CHC was 0.062 per 100,000 individuals in 2004 and 0.081 per 100,000 individuals in 2018, with an APC of 1.0% (P > 0.05). CHC displayed intermediate clinicopathological features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Race, tumor size, vascular invasion, extrahepatic invasion, distant metastasis, grade, surgery, and Metavir stage were confirmed as the independent predictors of cancer-specific survival. The constructed nomogram was well calibrated, which showed better discrimination power and higher net benefits than the current American Joint Committee on Cancer staging system. Patients with liver transplantation had better survival than those with hepatectomy, especially patients within the Milan Criteria (P=0.022 and P=0.015). There was no survival difference between liver transplantation and hepatectomy in patients beyond the Milan Criteria (P=0.340). Conclusion The morbidity of CHC remained stable between 2004 and 2018. The constructed nomogram could predict the prognosis with good performance, which was meaningful to individual treatment strategies optimization. CHC patients should also be considered as potential liver transplantation recipients, especially those within the Milan Criteria, but the finding still needs more evidence to be further confirmed.

Development of a competing risk nomogram for the prediction of cause-specific mortality in patients with thymoma: a population-based analysis.

BackgroundThis study was developed to assess the odds of cause-specific mortality and other types of mortality in thymoma patients. In addition, these analyses were leveraged to develop a comprehensive competing risk model-based nomogram capable of predicting cause-specific mortality as a result of thymoma.MethodsThymoma patients included within the Surveillance, Epidemiology, and End Results (SEER) database from 2004–2016 were identified, and the odds of cause-specific mortality due to thymoma and other forms of mortality for these patients were estimated. In addition, Fine and Gray’s proportional subdistribution hazard model was constructed, and a competing risk nomogram was developed using this model that was capable of predicting the odds of 3-, 5-, and 10-year cause-specific mortality in thymoma patients.ResultsIn total, 1,591 relevant cases in the SEER database were selected for analysis. In this patient cohort, the respective 5-year cumulative incidence rates for cause-specific mortality and mortality attributable to other causes were 12.4% and 8.2%. Variables significantly associated with cause-specific mortality included age, chemotherapy, surgery, and Masaoka stage. Additionally, the odds of other-cause-specific mortality rose with increasing patient age, and chemotherapy was correlated with other-cause-specific mortality. The competing risk nomogram that was developed exhibited good discriminative ability as a means of predicting cause-specific mortality, as evidenced by a concordance index (C-index) value of 0.84. Calibration curves further revealed excellent consistency between predicted and actual mortality when using this nomogram.ConclusionsIn summary, we herein assessed the odds of cause-specific and other-cause-specific mortality among thymoma patients, and we designed a novel nomogram capable of predicting cause-specific mortality for thymoma, providing a promising tool that may be of value in the context of individualized patient prognostic evaluation.

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study.

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.

Nomogram Predicting Cancer-Specific Death in Parotid Carcinoma: a Competing Risk Analysis.

PurposeMultiple factors have been shown to be tied to the prognosis of individuals with parotid cancer (PC); however, there are limited numbers of reliable as well as straightforward tools available for clinical estimation of individualized mortality. Here, a competing risk nomogram was established to assess the risk of cancer-specific deaths (CSD) in individuals with PC.MethodsData of PC patients analyzed in this work were retrieved from the Surveillance, Epidemiology, and End Results (SEER) data repository and the First Affiliated Hospital of Nanchang University (China). Univariate Lasso regression coupled with multivariate Cox assessments were adopted to explore the predictive factors influencing CSD. The cumulative incidence function (CIF) coupled with the Fine-Gray proportional hazards model was employed to determine the risk indicators tied to CSD as per the univariate, as well as multivariate analyses conducted in the R software. Finally, we created and validated a nomogram to forecast the 3- and 5-year CSD likelihood.ResultsOverall, 1,467 PC patients were identified from the SEER data repository, with the 3- and 5-year CSD CIF after diagnosis being 21.4% and 24.1%, respectively. The univariate along with the Lasso regression data revealed that nine independent risk factors were tied to CSD in the test dataset (n = 1,035) retrieved from the SEER data repository. Additionally, multivariate data of Fine-Gray proportional subdistribution hazards model illustrated that N stage, Age, T stage, Histologic, M stage, grade, surgery, and radiation were independent risk factors influencing CSD in an individual with PC in the test dataset (p < 0.05). Based on optimization performed using the Bayesian information criterion (BIC), six variables were incorporated in the prognostic nomogram. In the internal SEER data repository verification dataset (n = 432) and the external medical center verification dataset (n = 473), our nomogram was well calibrated and exhibited considerable estimation efficiency.ConclusionThe competing risk nomogram presented here can be used for assessing cancer-specific mortality in PC patients.

Predicting cancer-specific mortality in patients with parotid gland carcinoma by competing risk nomogram.

The aim of the present study was to establish a competing risk nomogram to predict parotid gland cancer-specific mortality (PGC-SM). Seven thousand nine hundred and sixty-two patients extracted from SEER database were randomly categorized into training and validation sets. The competing risk model was used to identify factors associated with PGC-SM. The nomogram was evaluated via concordance indexes (C-indexes), calibration plots, and decision curve analysis (DCA). Male, elderly, white, widowed, larger tumor, no surgery, advanced tumor grade, lymph node (LN) metastasis, adenocarcinoma (ADC), and higher TNM stage were associated with higher incidence of PGC-SM. Calibration plots showed that the nomogram was well calibrated. C-indexes for nomogram were 0.84 (95% CI: 0.81-0.86) and 0.84 (95% CI: 0.82-0.86) in training and validation sets, respectively. DCA demonstrated the clinical usefulness of nomogram. The competing risk nomogram shows high performance in predicting PGC-SM, which might enable clinicians formulate suitable treatment protocols for patients with parotid gland carcinoma (PGC).

Competing Nomogram for Late-Period Breast Cancer-Specific Death in Patients with Early-Stage Hormone Receptor-Positive Breast Cancer

BackgroundMore than half of early breast cancer recurrences occur after 5 years from the initial diagnosis. An individualized estimate of the risk of late-period breast cancer-specific death (LP-BCSD) after 5 years of endocrine therapy (ET) can improve decision-making for extended endocrine therapy (EET). Materials and MethodsA total of 147,059 eligible patients with breast cancer who survived 5+ years after diagnosis between 1990 and 2007 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses based on the competing risk regression model were used to evaluate predictive factors for high risk of LP-BCSD or late-period non-breast cancer-specific death (LP-non-BCSD). Significant factors were used to build a nomogram to individualize estimates of LP-BCSD or LP-non-BCSD. ResultsThe 5- and 10-year LP-BCSD rates were 5.7% and 10.1%, respectively, and the 5- and 10-year LP-non-BCSD rates were 6.7% and 15.5%, respectively. Young age, black race, single marital status, poor differentiation, large tumor size, lymph node metastasis, and estrogen receptor-positive/progesterone receptor-negative (ER+/PR-) status were independent predictive factors for high risk of LP-BCSD. Age was the most important factor for predicting high risk of LP-non-BCSD. The nomograms, which were based on significant factors identified by the competing risk regression model. A risk score system based on the competing risk nomogram was established to describe the relative risk of LP-BCSD and LP-non-BCSD. ConclusionThis study explored the novel endpoint of LP-BCSD for further clinical trials. The risk score system might be highly useful for patient counseling, especially in discussing EET options with elderly or comorbid patients.

A Competing Risk Nomogram for Predicting Cancer-Specific Death of Patients With Maxillary Sinus Carcinoma.

ObjectivesHerein, we purposed to establish and verify a competing risk nomogram for estimating the risk of cancer-specific death (CSD) in Maxillary Sinus Carcinoma (MSC) patients.MethodsThe data of individuals with MSC used in this study was abstracted from the (SEER) Surveillance, Epidemiology, and End Results data resource as well as from the First Affiliated Hospital of Nanchang University (China). The risk predictors linked to CSD were identified using the CIF (cumulative incidence function) along with the Fine-Gray proportional hazards model on the basis of univariate analysis coupled with multivariate analysis implemented in the R-software. After that, a nomogram was created and verified to estimate the three- and five-year CSD probability.ResultsOverall, 478 individuals with MSC were enrolled from the SEER data resource, with a 3- and 5-year cumulative incidence of CSD after diagnosis of 42.1% and 44.3%, respectively. The Fine-Gray analysis illustrated that age, histological type, N stage, grade, surgery, and T stage were independent predictors linked to CSD in the SEER-training data set (n = 343). These variables were incorporated in the prediction nomogram. The nomogram was well calibrated and it demonstrated a remarkable estimation accuracy in the internal validation data set (n = 135) abstracted from the SEER data resource and the external validation data set (n = 200). The nomograms were well-calibrated and had a good discriminative ability with concordance indexes (c-indexes) of 0.810, 0.761, and 0.755 for the 3- and 5-year prognosis prediction of MSC-specific mortality in the training cohort, internal validation, and external validation cohort, respectively.ConclusionsThe competing risk nomogram constructed herein proved to be an optimal assistant tool for estimating CSD in individuals with MSC.

Second Primary Malignancies in Patients With Hepatocellular Carcinoma: A Population-Based Analysis.

BackgroundSecond primary malignancy (SPM) is becoming a threat for the health of cancer survivors. However, data on the features and results of patients with hepatocellular carcinoma (HCC) with SPMs are scarce. This study aimed to explore the characteristics of HCC patients with SPMs and to screen HCC patients who are at a high risk of developing SPMs.MethodHCC patients diagnosed between 2000 and 2014 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed. Eligible patients were divided into the only one primary malignancy and SPM groups. The Fine-Gray proportional subdistribution hazards model was used to explore the risk factors of developing SPMs, and a competing-risk model was established to predict the probability of developing SPMs for HCC patients after initial diagnosis. The calibration curves, concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.ResultsA total of 40,314 HCC patients were identified, 1,593 (3.95%) of whom developed SPMs 2 months after the initial diagnosis with a maximum follow-up time of approximately 18 years. The 3-, 5-, and 10-year cumulative incidence of SPMs were 2.35%, 3.12%, and 4.51%, respectively. Age at initial diagnosis, extent of disease, tumor size, and treatment were identified as the independent risk factors of developing SPMs and integrated into the competing-risk nomogram. The C-index of the nomogram was 0.677 (95% confidence interval 0.676–0.678), and the calibration curves showed an excellent agreement between the nomogram prediction and the actual observations. Furthermore, DCA indicated that the nomogram had good net benefits in clinical scenarios.ConclusionsHCC survivors remain at a high risk of developing SPMs. The development of SPMs was associated with the clinical features and treatment strategies. A competing-risk nomogram was constructed to help surgeons identify the patients who are at a high risk of developing SPMs and contribute to the further management of SPMs.

Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis

ObjectiveThis study aimed to use a competing-risks model to establish a nomogram to accurately analyse the prognostic factors for upper tract urothelial carcinoma (UTUC) cancer-specific death (CSD).DesignRetrospective observational cohort study.SettingThe...

Model to predict cause\u2010specific mortality in patients with olfactory neuroblastoma: a competing risk analysis

PurposeThe main objective of this study was to evaluate the cumulative incidence of cause-specific mortality and other causes of mortality for patients with olfactory neuroblastoma (ONB). The secondary aim was to model the probability of cause-specific death and build a competing risk nomogram to predict cause-specific mortality for this disease.MethodsPatients with ONB from 1975 to 2016 were identified from the Surveillance, Epidemiology, and End Results database. We estimated the cumulative incidence function (CIF) for cause-specific mortality and other causes of mortality, and constructed the Fine and Gray’s proportional subdistribution hazard model, as well as a competing-risk nomogram based on Fine and Gray’s model, to predict the probability of cause-specific mortality for patients with ONB.ResultsAfter data selection, 826 cases were included for analysis. Five-year cumulative incidence of cause-specific mortality was 19.5% and cumulative incidence of other causes of mortality was 11.3%. Predictors of cause-specific mortality for ONB included tumor stage, surgery and chemotherapy. Age was most strongly predictive of other causes of mortality: patients aged > 60 years exhibited subdistribution hazard ratios of 1.063 (95 % confidence interval [CI] 1.05–1.08; p = 0.001). The competing risk nomogram for cause-specific mortality was well-calibrated, and had good discriminative ability (concordance index = 0.79).ConclusionsWe calculated the CIF of cause-specific mortality and other causes of mortality in patients with the rare malignancy ONB. We also built the first competing risk nomogram to provide useful individualized predictive information for patients with ONB.

Development and validation of a nomogram to predict cancer-specific survival of uveal melanoma

BackgroundUveal melanoma (UM) is a rare but aggressive cancer, which is the most common primary intraocular malignancy in adults. We aimed to develop and validate a competing risk nomogram to predict cancer-specific survival (CSS) of patients with UM, as well as compare its prognostic value with that of the American Joint Committee on Cancer (AJCC) staging system.MethodsData of patients diagnosed with UM from 2010 to 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We extracted and integrated significant prognostic factors based on competing risk regression to build a nomogram. The nomogram with an online prediction version was also created. The performance of the nomogram was evaluated using Harrell’s concordance index (C-index) and calibration plots. Receiver operating characteristic (ROC) curve was carried out to estimate clinical applicability of the model. Improvements in the predictive accuracy of our new model compared with AJCC staging system were estimated by calculating the relative integrated discrimination improvement (IDI) and the net reclassification improvement (NRI).ResultsA total of 839 eligible patients with primary UM were randomly assigned to a training cohort (588, 70%) and a validation cohort (251, 30%). Age, histological type, T stage and M stage were independent prognostic factors to predict CSS of UM and were incorporated in the nomogram. The calibration plots indicated that the 3- and 5-year CSS probabilities were consistent between the nomogram prediction and the actual observation. The C-index for this model was 0.778 (95% CI:0.756–0.800) and 0.786 (95% CI: 0.749–0.816) in the training cohort and validation cohort. Areas under the curve (AUCs) were 0.814, 0.771, and 0.792 in the training cohort, 0.788, 0.781 and 0.804 in the validation cohort, respectively. The NRI value in AJCC staging system was − 0.153 (95% CI -0.29 – − 0.041) for 3 years of follow-up and − 0.276 (95% CI -0.415 – − 0.132) for 5 years of follow-up. The IDI values for 3 and 5 years of follow-up in the AJCC staging system were − 0.021 (P = 0.076) and − 0.045 (P = 0.004), respectively.ConclusionsWe have developed and validated a competing risk nomogram to reliably predict cancer-specific survival of patients with UM. This convenient tool may be useful for evaluating cancer-specific prognosis.

Competing risk nomogram predicting cancer-specific mortality for endometrial cancer patients treated with hysterectomy.

BackgroundThe incidence of endometrial cancer has tended to increase in recent years. However, competing risk nomogram combining comprehensive factors for endometrial cancer patients treated with hysterectomy is still scarce. Therefore, we aimed to build a competing risk nomogram predicting cancer‐specific mortality for endometrial cancer patients treated with hysterectomy.MethodsPatients diagnosed with endometrial cancer between 2010 and 2012 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. Competing risk model was performed to select prognostic variables to build the competing risk nomogram to predict the cumulative 3‐ and 5‐year incidences of endometrial cancer‐specific mortality. Harrell's C‐index, receiver operating characteristic (ROC) curve, and calibration plot were used in the internal validation. And decision curve analysis was applied to evaluate clinical utility.ResultsA total of 10,447 patients were selected for analysis. The competing risk nomogram identified eight prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, and number of regional nodes positive. The C‐index of the competing risk nomogram was 0.857 (95% confidence interval [CI]: 0.854–0.859), and the calibration plots were adequately fitted. When the threshold probabilities were between 1% and 57% for 3‐year prediction and between 2% and 67% for 5‐year prediction, the competing risk nomogram was of good clinical utility.ConclusionsA competing risk nomogram for endometrial cancer patients treated with hysterectomy was successfully built and internally validated. It was an accurately predicted and clinical useful tool, which could play an important role in consulting and health care management of endometrial cancer patients.

The Role of Adjuvant Chemotherapy in Metaplastic Breast Carcinoma: A Competing Risk Analysis of the SEER Database.

Purpose: Chemotherapy is the clinically recommended treatment for patients with operable metaplastic breast carcinoma (MBC); however, its impact remains controversial. This study investigated the possible role of chemotherapy in the treatment of MBC.Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify the operable MBC patients. The competing risk analysis along with the propensity score matching (PSM) method was performed to evaluate the effect of chemotherapy. Moreover, a competing risk nomogram was built to identify prognosis in patients with MBC.Results: Of the 1137 patients with MBC, 775 received chemotherapy and 362 did not receive chemotherapy. The 5-year cumulative incidence of breast cancer-specific death (BCSD) showed similar outcomes in both the Chemo and No-Chemo groups (21.1 vs. 24.3%, p = 0.57). Chemotherapy showed no apparent association with BCSD (HR, 1.07; 95% CI, 0.72–1.60; p = 0.72), even after subgroup analysis or PSM. Race, tumor size, lymph node status, and radiation were identified as the significant factors for MBC after a penalized variable selection process. In addition, a competing risk nomogram showed relatively good accuracy of prediction with a C-index of 0.766 (95% CI, 0.700–0.824).Conclusion: Our findings demonstrated that chemotherapy did not improve BCSD for operable MBC patients. Thus, it may indicate the need to reduce exposure to the current chemotherapy strategies for patients with resectable MBC. Additionally, some novel treatment strategies are required urgently to identify and target the potential biomarkers.

Competing-Risk Nomograms for Predicting the Prognosis of Patients With Infiltrating Lobular Carcinoma of the Breast

BackgroundInfiltrating lobular carcinoma (ILC) is the second most common histologic subtype of breast cancer. We assessed the rates of cause-specific death in ILC patients with the aim of establishing competing-risk nomograms for predicting their prognosis. Patients and MethodsData on ILC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function was used to calculate the cumulative incidence rates of cause-specific death, and Gray's test was applied to test the differences in cumulative incidence rates among groups. We then identified independent prognostic factors by applying the Fine–Gray proportional subdistribution hazard analysis method and established nomograms based on the results. Calibration curves and the concordance index were employed to validate the nomograms. ResultsThe study enrolled 11,361 patients. The 3-, 5-, and 10-year overall cumulative incidence rates for those who died of ILC were 3.1%, 6.2%, and 12.2%, respectively, whereas the rates for those who died from other causes were 3.2%, 5.8%, and 14.1%. Age, marriage, grade, size, regional node positivity, American Joint Committee on Cancer M stage, progesterone receptor, and surgery were independent prognostic factors for dying of ILC, whereas the independent prognostic factors for dying of other causes were age, race, marriage, size, radiation, and chemotherapy. The nomograms were well calibrated and had good discrimination ability. ConclusionWe applied competing-risk analysis to ILC patients based on the SEER database and established nomograms that perform well in predicting the cause-specific death rates at 3, 5, and 10 years after the diagnosis.

Evaluating and Balancing the Risk of Breast Cancer-Specific Death and Other Cause-Specific Death in Elderly Breast Cancer Patients.

Purpose: The dilemma of undertreatment and overtreatment of elderly breast cancer patients is common. This study aimed to investigate clinicopathological features, treatment modalities, and survival in women diagnosed with breast cancer at age 70 years or over, and to assist clinicians in developing individualized treatment plans by balancing the risks of breast cancer-specific death (BCSD) and other cause-specific death (OCSD).Methods: This retrospective study included 420 women who were diagnosed with pathologically confirmed invasive breast cancer at age 70 years or older from January 2008 to December 2015 at Peking University People's Hospital (PKUPH). We collected baseline health status, tumor characteristics, treatment choices, and outcomes and created nomograms for clinicians to estimate individualized BCSD and OCSD risk directly.Results: During a median follow-up of 71.5 months (range 2 to 144 months) in patients with stage I–III tumors, breast cancer specific survival (BCSS) was 92.4% (376/407) and overall survival (OS) was 78.1% (318/407). There were 89 deaths, and 65.2% (58/89) were non-breast cancer related. Upon multivariate analysis by Cox regression model, tumor size, positive lymph nodes, Ki-67, and surgery were independent predictors of BCSS, and comorbidities, positive lymph nodes, Ki-67, surgery, and endocrine therapy were independent predictors of OS. Propensity score weighted (PSW) was applied to analyze therapeutic efficacy, and there was BCSS and OS benefit with surgery (both p < 0.001), BCSS benefit with chemotherapy (p = 0.029), BCSS and OS benefit with endocrine therapy (p = 0.006 and 0.004), and neither BCSS nor OS benefit with radiotherapy (RT) (p = 0.348 and 0.289). Competing-risk nomograms were developed to estimate cumulative mortality probabilities for BCSD and OCSD for individual patients according to clinicopathologic characteristics and treatments. The calibration curves displayed exceptionally, with C-indexes 0.714 for BCSD and 0.717 for OCSD.Conclusions: Older patients had greater risk of dying from non-breast cancer causes. Surgery, chemotherapy, and endocrine therapy were associated with improved survival. Competing risk nomograms allowed individual assessment of BCSD and OCSD, based on clinicopathological characteristics and treatment options, and can be used as a tool to help in choosing appropriate treatment strategies.This study was approved by the Peking University People's Hospital Research Ethics Board on September 4, 2018.

Development and validation of a novel competing risk model for predicting survival of esophagogastric junction adenocarcinoma: a SEER population-based study and external validation

BackgroundAdenocarcinoma in Esophagogastric Junction (AEG) is a severe gastrointestinal malignancy with a unique clinicopathological feature. Hence, we aimed to develop a competing risk nomogram for predicting survival for AEG patients and compared it with new 8th traditional tumor-node-metastasis (TNM) staging system.MethodsBased on data from the Surveillance, Epidemiology, and End Results (SEER) database of AEG patients between 2004 and 2010, we used univariate and multivariate analysis to filter clinical factors and then built a competing risk nomogram to predict AEG cause-specific survival. We then measured the clinical accuracy by comparing them to the 8th TNM stage with a Receiver Operating Characteristic (ROC) curve, Brier score, and Decision Curve Analysis (DCA). External validation was performed in 273 patients from China National Cancer Center.ResultsA total of 1755 patients were included in this study. The nomogram was based on five variables: Number of examined lymph nodes, grade, invasion, metastatic LNs, and age. The results of the nomogram was greater than traditional TNM staging with ROC curve (1-year AUC: 0.747 vs. 0.641, 3-year AUC: 0.761 vs. 0.679, 5-year AUC: 0.759 vs. 0.682, 7-year AUC: 0.749 vs. 0.673, P < 0.001), Brier score (3-year: 0.198 vs. 0.217, P = 0.012; 5-year: 0.198 vs. 0.216, P = 0.008; 7-year: 0.199 vs. 0.215, P = 0.014) and DCA. In external validation, the nomogram also showed better diagnostic value than traditional TNM staging and great prediction accuracy.ConclusionWe developed and validated a novel nomogram and risk stratification system integrating clinicopathological characteristics for AEG patients. The model showed superior prediction ability for AEG patients than traditional TNM classification.

Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram

Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis (P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery.

Cause-specific mortality of low and selective intermediate-risk prostate cancer patients with active surveillance or watchful waiting.

BackgroundActive surveillance or watchful waiting (AS/WW) is increasingly being used as an alternative strategy to radical prostatectomy or radiation therapy for appropriately selected patients with prostate cancer (PCa). However, the prognosis of low-risk and selective intermediate-risk PCa patients after AS/WW is poorly defined. In this study we reviewed the patients registered in the Surveillance, Epidemiology, and End Results (SEER) Program to establish a competing risk nomogram for the prediction of prostate cancer-specific mortality (PCSM).MethodsThe information of patients undergoing AS/WW in the SEER program from 2004 to 2015 was obtained. All patients were ISUP (International Society of Urological Pathology) grade 1 or 2 PCa and also fulfilled the National Comprehensive Cancer Network’s definition of low-risk PCa [prostate specific antigen (PSA) <10 ng/mL and cT2aN0M0 or less)]. A competing risk nomogram was used to analyze the association of tumor characteristics with PCSM and non-PCSM among the PCa patients with AS/WW. All cases were randomly divided into a training cohort and a validation cohort (1:1). A competing risk nomogram was constructed to predict PCSM in PCa patients with AS/WW. The performance of the PCSM nomogram was evaluated using the concordance index (C-index) and calibration curve.ResultsA total of 30,538 PCa patients were identified as low risk or selective intermediate risk with AS/WW. The 10-year cumulative incidence of death from prostate cancer and death from other cause were 2.8% (95% CI: 2.4–3.1%) and 19.3% (95% CI: 17.8–20.5%), respectively. Variables associated with PCSM included age, marital status, PSA, and ISUP grade. The PCSM nomogram had a good performance in both the training and validation cohorts, with a C-index of 0.744 (95% CI: 0.700–0.781, P<0.001) and 0.738 (95% CI: 0.700–0.777, P<0.001), respectively.ConclusionsOverall, the prognosis was favorable for the low- and selective intermediate-risk PCa patients with AS/WW. The competing risk nomogram yielded a good performance in identifying subgroups of patients with a higher risk of PCSM and potential candidates for AS/WW.

Recurrence risk stratification based on a competing-risks nomogram to identify patients with esophageal cancer who may benefit from postoperative radiotherapy.

Background:A reliable model is needed to estimate the risk of postoperative recurrence and the benefits of postoperative radiotherapy (PORT) in patients with thoracic esophageal squamous cell cancer (TESCC).Methods:The study retrospectively reviewed 3652 TESCC patients in stage IB-IVA after radical esophagectomy, with or without PORT. In one institution as the training cohort (n = 1620), independent risk factors associated with locoregional recurrence (LRR), identified by the competing-risks regression, were used to establish a predicting nomogram, which was validated in an external cohort (n = 1048). Area under curve (AUC) values of receiver operating characteristic curves were calculated to evaluate discrimination. Risk stratification was conducted using a decision tree analysis based on the cumulative point score of the LRR nomogram. After balancing the baseline of characteristics between treatment groups by inverse probability of treatment weighting, the effect of PORT was evaluated in each risk group.Results:Sex, age, tumor location, tumor grade, and N category were identified as independent risk factors for LRR and added into the nomogram. The AUC values were 0.638 and 0.706 in the training and validation cohorts, respectively. Three risk groups were established. For patients in the intermediate- and high-risk groups, PORT significantly improved the 5-year overall survival by 10.2% and 9.4%, respectively (p < 0.05). Although PORT was significantly associated with reduced LRR in the low-risk group, overall survival was not improved.Conclusion:The nomogram can effectively estimate the individual risk of LRR, and patients in the intermediate- and high-risk groups are highly recommended to undergo PORT.

Novel survival nomograms for patients with lung metastatic clear cell renal cell carcinoma: A population-based study.

Survival heterogeneity is observed among renal cell carcinoma (RCC) patients with metastases in different organs. Moreover, almost all previous prognostic nomograms based on data from metastatic RCC patients did not take competing events, such as death from cerebrovascular and heart diseases, into account. We aimed to construct novel prognostic nomograms for patients with lung metastatic clear cell RCC (LMCCRCC).Data of 712 non-Hispanic white LMCCRCC patients registered in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed. Nomograms for predicting overall survival (OS) and disease-specific survival (DSS) were established using the Cox approach and Fine and Gray approach, respectively, and their performances were assessed using the concordance index (C-index), calibration plots, and an independent cohort comprising 181 Hispanic patients.Sex, tumor grade, T stage, N stage, presence or absence of bone metastases, and presence or absence of brain metastases were independent predictors for both OS and DSS. Additionally, presence or absence of liver metastases was an independent predictor only for DSS. Meanwhile, age at diagnosis was independently associated with OS. The C-indexes of the nomograms were 0.702 for OS and 0.723 for DSS in internal validation. In external validation, the C-indexes were 0.700 for OS and 0.708 for DSS. Both internal and external calibration plots showed excellent consistency between the prediction and the observation.The current study developed a novel nomogram for predicting individual OS in LMCCRCC patients. Moreover, we constructed an effective competing risk nomogram for predicting their individual DSS for the first time.