Nomogram For Survival Of Patients Research Articles

Survival nomograms for patients with retroperitoneal soft tissue sarcoma based on the SEER database and an external cohort.

Retroperitoneal soft tissue sarcoma (RPS) is a quite rare disease,and new nomograms need to be constructed to predict the overall survival (OS) and cancer specific survival (CSS) of RPS patients. The clinical data of patients with RPS were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and authors' hospital. The LASSO method and COX proportional hazard regression were used to screen independent risk factors which were used to develop nomograms. Nomograms was evaluated in terms of discrimination by consistency index (C-index) and calibration curve. Decision curve analysis (DCA) and integrated discrimination index (IDI) were performed to assess net benefit and the improvement of model, respectively. Kaplan-Meier method and log-rank test were applied to compare the survival difference between groups. A total of 1164 cases were enrolled which were divided into training cohort (n = 814) and internal validation cohort (n = 350) at a 7:3 ratio. The discrimination of nomograms were good with C-index of 0.728 (95%CI = 0.704-0.752, R2 = 0.270), 0.754 (95%CI = 0.729-0.779, R2 = 0.281) for OS and CSS, respectively. Calibration curve showed good predictive accuracy of nomograms both in internal and external validation cohort, and IDI indicated that nomograms perform well than AJCC stage. Kaplan Meier curve and log-rank test uncovered statistically significance survival difference between high- and low-risk groups with P < 0.001 in terms of OS and CSS. Novel nomograms predicting the OS and CSS of RPS patients perform well in discrimination, calibration, clinical benefit and IDI. These nomograms may facilitate risk stratification and making clinical decision.

Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort

BackgroundOn average, 5–10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) in these patients. MethodsThe nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance, Epidemiology, and End Results (SEER) database. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), and calibration curve. Decision curve analysis (DCA) was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China. ResultsMultivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis, pathological type, histological grade, T stage, N stage, molecular subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.688 (95% confidence interval [CI], 0.682–0.694) in the training cohort and 0.875 (95% CI, 0.816–0.934) in the validation cohort. The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts, respectively. Calibration curves showed favorable consistency between the predicted and actual survival probabilities. Additionally, the DCA curve produced higher net gains than by the AJCC-TNM staging system. Finally, risk stratification can accurately identify groups of patients with de novo MBC at different risk levels. ConclusionsThe nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC. Other populations from different countries or prospective studies are needed to further validate the nomogram.

Survival nomogram for patients with thymic squamous cell carcinoma, based on the SEER database and an external validation cohort

ObjectiveThis study aimed to construct a nomogram to effectively predict the 3 years and 5 years overall survival of patients with thymic squamous cell carcinoma (TSCC).MethodFrom 2000 to 2019, a total of 355 patients with TSCC were enrolled in our research from the Surveillance, Epidemiology, and End Results (SEER) database and used as the training cohort. 106 patients were included from the Zhejiang Cancer Hospital, for the external validation cohort. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. The discrimination and calibration of the nomogram were evaluated by C-index and curve of calibration. The two cohorts were divided into low-risk and high-risk subgroups based on the median risk score.ResultsAge (p = 0.002), stage (p = 0.003), surgery therapy (p < 0.001), and radiotherapy (p = 0.030) were the independent prognostic factors for overall survival and were incorporated in the prognostic model. The discrimination of the nomogram revealed a good prognostic accuracy and clinical applicability as indicated by C-index values of 0.696 (95% confidence interval [CI] 0.676–0.716) and 0.717 (95% CI 0.640–0.794) for the training cohort and external validation cohort, respectively. In addition, the two cohorts were divided into a high-risk group and a low-risk group according to the median risk score. Significant differences in overall survival were observed between the high-risk and low-risk groups in the training (p < 0.0001) and external validation cohort (p < 0.0001).ConclusionWe developed a nomogram to predict 3- and 5 year survival rate for TSCC. This nomogram provides a convenient and reliable tool for assessing the condition of patients with TSCC and assisting clinicians in making decisions.

Survival Nomogram for Patients With Locally Advanced Breast Cancer Undergoing Immediate Breast Reconstruction: A SEER Population-Based Study

Introduction/BackgroundThis study aimed to construct a nomogram to provide prognostic references for patients with locally advanced breast cancer (LABC) to receive immediate breast reconstruction (IBR). Materials and MethodsAll data were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and best subset regression (BSR), separately followed by backward stepwise multivariable Cox, were used to construct the nomogram. Risk stratification was established after validation. ResultsA total of 6,285 patients were enrolled to generate the training group (n = 3,466) and the test group (n = 2,819) by geographical split. Age, marital status, grade, T staging, N staging, radiotherapy, chemotherapy, estrogen receptor status (ER), progesterone receptor status (PR) and human epidermal growth factor receptor type 2 status (HER2) were used to fit the nomogram. The overall Harrell's concordance index (C-index) was 0.772 in the training group and 0.762 in the test group. The area under the receiver operator characteristic curves (AUC) at 3-year and 5-year were respectively 0.824 and 0.720 in the training group, 0.792 and 0.733 in the test group. The calibration curves showed great consistency in both groups. A dynamic nomogram (https://dcpanfromsh.shinyapps.io/NomforLABCafterIBR/) was developed. ConclusionA nomogram was developed and validated that predicts prognosis more accurately than the AJCC 7th stage and can be used as a reference for decision-making in LABC patients receiving IBR.

Development and validation of a survival nomogram for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction based on real-world data

BackgroundThe clinical staging systems for adenocarcinoma of the esophagogastric junction (AEG) are controversial. We aimed to propose a prognostic nomogram based on real-world data for predicting survival of Siewert type II/III AEG patients after surgery.MethodsA total of 396 patients with Siewert type II/III AEG diagnosed and treated at the Center for Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-sen University, from June 2009 to June 2017 were enrolled. The original data of 29 variables were exported from the electronic medical records system. The nomogram was established based on multivariate Cox regression coefficients, and its performance was measured using Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve analysis and calibration curve.ResultsA nomogram was constructed based on nine variables. The C-index for overall survival (OS) prediction was 0.76 (95% CI, 0.72 to 0.80) in the training cohort, in the validation-1 cohort was 0.79 (95% CI, 0.72 to 0.86), and 0.73 (95% CI, 0.67 to 0.80) in the validation-2 cohort. Time-dependent ROC curves and calibration curves in all three cohorts showed good prognostic predictive accuracy. We further proved the superiority of the nomogram in predictive accuracy for OS to pathological TNM (pTNM) staging system and other independent prognostic factors. Kaplan-Meier survival curves demonstrated the pTNM stage, grade of differentiation, positive lymph node, log odds of positive lymph node and organ invasion were prognostic factors with good discriminative ability.ConclusionThe established nomogram demonstrated a more precise prognostic prediction for patients with Siewert type II/III AEG.

Survival nomogram for patients with bone metastatic renal cell carcinoma: A population-based study.

ABSTRACTPurpose:Increased attention has been focused on the survival of renal cell carcinoma (RCC) patients with bone metastasis. This study proposed to establish and evaluate a nomogram for predicting the overall survival (OS) and cancer-specific survival (CSS) of RCC patients with bone metastasis.Materials and Methods:RCC patients with bone metastasis between 2010 and 2015 were captured from the surveillance, epidemiology and end results (SEER) database. Univariate and multivariate cox regressions were performed to assess the effects of clinical variables on OS and CSS. The nomogram based on the Cox hazards regression model was developed. Concordance index (C-index) and calibration curve were performed to evaluate the accuracy of nomogram models, receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were conducted to assess the predict performance.Results:A total of 2.471 eligible patients were enrolled in this study. The patients were assigned to primary (n=1.672) and validation (n=799) cohorts randomly. The 1-, 2-, and 3-year OS and CSS nomogram models were constructed based on age at diagnosis, sex, marital status, pathological grade, T-stage, N-stage, brain/liver/lung metastasis, surgery, radiotherapy and chemotherapy. The c for OS and CSS prediction was 0.730 (95% confidence interval [CI]: 0.719-0.741) and 0.714 (95%CI:0.702-0.726). The calibration curves showed significant agreement between nomogram models and actual observations. ROC and DCA indicated nomograms had better predict performance.Conclusions:The nomograms for predicting prognosis provided an accurate prediction of OS and CSS in RCC patients with bone metastasis, and contributed clinicians to optimize individualized treatment plans.

Survival after palliative radiation therapy for cancer: The METSSS model

BackgroundWe propose a predictive model that identifies patients at greatest risk of death after palliative radiotherapy, and subsequently, can help medical professionals choose treatments that better align with patient choice and prognosis. MethodsThe National Cancer Database was queried for recipients of palliative radiotherapy during first course of treatment. Cox regression models and adjusted hazard ratios with 95% confidence intervals were used to evaluate survival predictors. The mortality risk index was calculated using predictors from the estimated Cox regression model, with higher values indicating higher mortality risk. Based on tertile cutpoints, patients were divided into low, medium, and high risk groups. ResultsA total of 68,505 patients were included from 2010-2014, median age 65.7 years. Several risk factors were found to predict survival: (1) location of metastases (liver, bone, lung, and brain); (2) age; (3) tumor primary (prostate, breast, lung, other); (4) gender; (5) Charlson-Deyo comorbidity score; and (6) radiotherapy site. The median survival times were 11.66 months, 5.09 months, and 3.28 months in the low (n=22,621), medium (n=22,638), and high risk groups (n=22,611), respectively. A nomogram was created and validated to predict survival, available online, https://tinyurl.com/METSSSmodel. Harrel’s C-index was 0.71 and receiver operator characteristic area under the curve was 0.76 at 4 years. ConclusionWe created a predictive nomogram for survival of patients receiving palliative radiotherapy during their first course of treatment (named METSSS), based on Metastases location, Elderly (age), Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy.

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.

Survival nomogram for patients with upper tract recurrence after resection for localized bladder urothelial carcinoma.

Aim: To develop a survival nomogram for patients with upper tract recurrence (UTR) afterresection forlocalized bladder urothelial carcinoma (BUC). Methods: The data of 361 patients with UTR after resection for BUC registered in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed. The nomogram was established using the Fine and Gray methodand its predictive accuracy was assessed using the concordance index. The nomogram was calibrated by comparing the predicted and actual survival. Results: The concordance index of the nomogram was 0.746 (95% CI: 0.733-0.759). Excellent agreement was observed between the predicted and actual survival in all calibration plots. Conclusion: This study describesthe first survival nomogram for patients experienced UTR after resection for BUC.

Survival nomogram for patients with metastatic siewert type II adenocarcinoma of the esophagogastric junction: a population-based study

ABSTRACT Background The aim of this study was to construct a nomogram to predict the survival of patients with metastatic Siewert Type II adenocarcinomas of the esophagogastric junction (AEG). Methods Patients were identified using the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was performed to assess the prognostic factors. A nomogram comprising independent prognostic factors was established and evaluated using C-indexes, calibration curves, and decision curve analyses. Results In total 1616 eligible patients were enrolled. Race, age, bone metastasis, liver metastasis, lung metastasis, other metastasis sites, and distant lymph nodes metastasis were independent prognostic factors and were integrated to construct the nomogram. The nomogram had a C-index of 0.590 (95% CI: 0.569–0.611) in the training cohort and 0.569 (95% CI: 0.532–0.606) in the validation cohort. The calibration plots for the probabilities of 6-month and 1-year overall survival demonstrated there was an optimum between nomogram prediction and actual observation. Conclusion We developed and validated a nomogram to predict individual prognosis for patients with metastatic Siewert Type II AEG, and the risk stratification system based on the nomogram could effectively stratify the patients into two risk subgroups, which can help clinicians accurately predict mortality risk and recommend personalized treatment modalities.

A prognostic nomogram for the cancer-specific survival of patients with upper-tract urothelial carcinoma based on the Surveillance, Epidemiology, and End Results Database

BackgroundThe aim of this study was to establish a comprehensive nomogram for the cancer-specific survival (CSS) of patients with upper-tract urothelial carcinoma (UTUC) and compare it with the traditional American Joint Committee on Cancer (AJCC) staging system in order to determine its reliability.MethodsThis study analyzed 9505 patients with UTUC in the Surveillance, Epidemiology, and End Results (SEER) database. R software was used to randomly divided the patients in a 7-to-3 ratio to form a training cohort (n = 6653) and a validation cohort (n = 2852). Multivariable Cox regression was used to identify predictive variables. The new survival model was compared with the AJCC prognosis model using the concordance index (C-index), the area under the time-dependent receiver operating characteristics curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA).ResultsWe have established a nomogram for determining the 3-, 5-, and 8-year CSS probabilities of UTUC patients. The nomogram indicates that the AJCC stage has the greatest influence on CSS in UTUC, followed by the age at diagnosis, surgery status, tumor size, radiotherapy status, histological grade, marital status, chemotherapy status, race, and finally sex. The C-index was higher for the nomogram than the AJCC staging system in both the training cohort (0.785 versus 0.747) and the validation cohort (0.779 versus 0.739). Calibration plotting demonstrated that the model has good calibration ability. The AUC, NRI, IDI, and DCA of the nomogram showed that it performs better than the AJCC staging system alone.ConclusionsThis study is the first to establish a comprehensive UTUC nomogram based on the SEER database and evaluate it using a series of indicators. Our novel nomogram can help clinical staff to predict the 3-, 5-, and 8-year CSS probabilities of UTUC patients more accurately than using the AJCC staging system.

Survival nomogram for patients with initially diagnosed metastatic non-small-cell lung cancer: a SEER-based study.

Aim: Prognosis of patients with metastatic non-small-cell lung cancer differ widely. Methods: All patients were randomly divided into training or validation cohort. Cox-regression analyses were conducted to select independent predictors. We built a nomogram by R code and evaluated the accuracy and the reliability of the model using C-index, calibration curves and decision curve analyses. We made a risk classification system based on the nomogram. Results: In the validation cohort, C-index was 0.729 and 0.738 for 1- and 2-year overall survival. Calibration plots and decision curve analyses presented great prognostic accuracy and clinical applicability. Its prognostic accuracy preceded the American Joint Committee on Cancer staging with evaluated integrated discrimination improvement. Conclusion: The model can be a practical tool in treatment decision and individual counseling.

Nomograms for predicting survival in patients with metastatic gastric adenocarcinoma who undergo palliative gastrectomy

BackgroundRecently, evidence has emerged that palliative gastrectomy in patients with stage IV gastric cancer may offer some survival benefits. However, the decision whether to perform primary tumor surgery remains challenging for surgeons, and investigations into models that are predictive of prognosis are scarce. Current study aimed to develop and validate prognostic nomograms for patients with metastatic gastric adenocarcinoma treated with palliative gastrectomy.MethodsThe development dataset comprised 1186 patients from the Surveillance, Epidemiology, and End Results Program who were diagnosed with metastatic gastric adenocarcinoma in 2004–2011, while the validation dataset included 407 patients diagnosed in 2012–2015. Variables were incorporated into a Cox proportional hazards model to identify independent risk factors for survival. Both pre- and postoperative nomograms for predicting 1- or 2-year survival probabilities were constructed using the development dataset. The concordance index (c-index) and calibration curves were plotted to determine the accuracy of the nomogram models. Finally, the cut-off value of the calculated total scores based on preoperative nomograms was set and validated by comparing survival with contemporary cases without primary tumor surgery.ResultsAge, tumor size, location, grade, T stage, N stage, metastatic site, scope of gastrectomy, number of examined lymph node(s), chemotherapy and radiotherapy were risk factors of survival and were included as variables in the postoperative nomogram; the c-indices of the development and validation datasets were 0.701 (95% confidence interval [CI]: 0.693–0.710) and 0.699 (95% CI: 0.682–0.716), respectively. The preoperative nomogram incorporated age, tumor size, location, grade, depth of invasion, regional lymph node(s) status, and metastatic site. The c-indices for the internal (bootstrap) and external validation sets were 0.629 (95% CI: 0.620–0.639) and 0.607 (95% CI: 0.588–0.626), respectively. Based on the preoperative nomogram, patients with preoperative total score > 28 showed no survival benefit with gastrectomy compared to no primary tumor surgery.ConclusionsOur survival nomograms for patients with metastatic gastric adenocarcinoma undergoing palliative gastrectomy can assist surgeons in treatment decision-making and prognostication.

Postoperative nomogram for survival of patients with retroperitoneal sarcoma treated with curative intent

BackgroundCurrent American Joint Committee on Cancer retroperitoneal sarcoma (RPS) staging is not representative of patients with RPS specifically and has limited discriminative power. Our objective was to develop a RPS disease-specific nomogram capable of stratifying patients based on probability of overall survival (OS) after resection. Patients and methodsIn all, 1118 RPS patients were evaluated at our institution (1996–2006). Patients with resectable, nonmetastatic disease were selected (n = 343) and baseline, treatment and outcome variables were retrieved. A nomogram was created and its performance was evaluated by calculating its discrimination (concordance index) and calibration and by subsequent internal validation. ResultsMedian follow-up and OS were 50 and 59 months, respectively. Independent predictors of OS were included in the nomogram: age (≥65), tumor size (≥15 cm), type of presentation (primary versus recurrent), multifocality, completeness of resection and histology. The concordance index was 0.73 [95% confidence interval (CI) 0.71–0.75] and the calibration was excellent, with all observed outcomes within the 95% CI of each predicted survival probability. ConclusionsA RPS-specific postoperative nomogram was developed. It improves RPS staging by allowing a more dynamic and robust disease-specific risk stratification. This prognostic tool can help in patient counseling and for selection of high-risk patients that may benefit from adjuvant therapies or inclusion into clinical trials.

662: Internal Validation of a Competing-Risks Survival Nomogram for Patients Treated with Radical Prostatectomy (RP) for Clinically Prostate Cancer (PCA)

662: Internal Validation of a Competing-Risks Survival Nomogram for Patients Treated with Radical Prostatectomy (RP) for Clinically Prostate Cancer (PCA)

1140: Prognostic Nomogram for Disease-Specific Survival of Patients with Untreated Androgen Independent Prostate Cancer

1140: Prognostic Nomogram for Disease-Specific Survival of Patients with Untreated Androgen Independent Prostate Cancer