scholarly journals Nomograms Forecasting Long-Term Overall and Cancer Specific Survival of Patients With Head and Neck Neuroendocrine Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Ouying Yan ◽  
Wenji Xie ◽  
Haibo Teng ◽  
Shengnan Fu ◽  
Yanzhu Chen ◽  
...  
Keyword(s):  
Head And Neck ◽  
Neuroendocrine Carcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Receiver Operating Curve ◽  
Survival Prediction ◽  
Validation Group ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Cancer Hospital

BackgroundThe purpose of this retrospective analysis was to build and validate nomograms to predict the cancer-specific survival (CSS) and overall survival (OS) of head and neck neuroendocrine carcinoma (HNNEC) patients.MethodsA total of 493 HNNEC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and 74 HNNEC patients were collected from the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital (HCH) between 2008 and 2020. Patients from SEER were randomly assigned into training (N=345) and internal validation (N=148) groups, and the independent data group (N=74) from HCH was used for external validation. Independent prognostic factors were collected using an input method in a Cox regression model, and they were then included in nomograms to predict 3‐, 5‐, and 10‐year CSS and OS rates of HNNEC patients. Finally, we evaluated the internal and external validity of the nomograms using the consistency index, while assessing their prediction accuracy using calibration curves. A receiver operating curve (ROC) was also used to measure the performance of the survival models.ResultsThe 3-, 5-, and 10-year nomograms of this analysis demonstrated that M classification had the largest influence on CSS and OS of HNNEC, followed by the AJCC stage, N stage, age at diagnosis, sex/gender, radiation therapy, and marital status. The training validation C-indexes for the CSS and OS models were 0.739 and 0.713, respectively. Those for the internal validation group were 0.726 and 0.703, respectively, and for the external validation group were 0.765 and 0.709, respectively. The area under the ROC curve (AUC) of 3-, 5-, and 10-year CSS and OS models were 0.81, 0.82, 0.82, and 0.78, 0.81, and 0.82, respectively. The C-indexes were all higher than 0.7, indicating the high accuracy ability of our model’s survival prediction.ConclusionsIn this study, prognosis nomograms in HNNEC patients were constructed to predict CSS and OS for the first time. Clinicians can identify patients’ survival risk better and help patients understand their survival prognosis for the next 3, 5, and 10 years more clearly by using these nomograms.

scholarly journals Comparing the characteristics and predicting the survival of patients with head and neck melanoma versus body melanoma: a population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuxin Ding ◽  
Runyi Jiang ◽  
Yuhong Chen ◽  
Jing Jing ◽  
Xiaoshuang Yang ◽  
...  
Keyword(s):  
Head And Neck ◽  
Health Management ◽  
External Validation ◽  
Area Under The Curve ◽  
Population Based ◽  
Survival Prediction ◽  
Cancer Specific Survival ◽  
Cox Models ◽  
Lentigo Maligna Melanoma ◽  
Clinical Tools

Abstract Background Previous studies reported cutaneous melanoma in head and neck (HNM) differed from those in other regions (body melanoma, BM). Individualized tools to predict the survival of patients with HNM or BM remain insufficient. We aimed at comparing the characteristics of HNM and BM, developing and validating nomograms for predicting the survival of patients with HNM or BM. Methods The information of patients with HNM or BM from 2004 to 2015 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The HNM group and BM group were randomly divided into training and validation cohorts. We used the Kaplan-Meier method and multivariate Cox models to identify independent prognostic factors. Nomograms were developed via the rms and dynnom packages, and were measured by the concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration plots. Results Of 70,605 patients acquired, 21% had HNM and 79% had BM. The HNM group contained more older patients, male sex and lentigo maligna melanoma, and more frequently had thicker tumors and metastases than the BM group. The 5-year cancer-specific survival (CSS) and overall survival (OS) rates were 88.1 ± 0.3% and 74.4 ± 0.4% in the HNM group and 92.5 ± 0.1% and 85.8 ± 0.2% in the BM group, respectively. Eight variables (age, sex, histology, thickness, ulceration, stage, metastases, and surgery) were identified to construct nomograms of CSS and OS for patients with HNM or BM. Additionally, four dynamic nomograms were available on web. The internal and external validation of each nomogram showed high C-index values (0.785–0.896) and AUC values (0.81–0.925), and the calibration plots showed great consistency. Conclusions The characteristics of HNM and BM are heterogeneous. We constructed and validated four nomograms for predicting the 3-, 5- and 10-year CSS and OS probabilities of patients with HNM or BM. These nomograms can serve as practical clinical tools for survival prediction and individual health management.

scholarly journals Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients of colorectal cancer

2019 ◽  
Vol 50 (3) ◽  
pp. 261-269
Author(s):  
Jieyun Zhang ◽  
Yue Yang ◽  
Xiaojian Fu ◽  
Weijian Guo
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Prognosis ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Development And Validation

Abstract Purpose Nomograms are intuitive tools for individualized cancer prognosis. We sought to develop a clinical nomogram for prediction of overall survival and cancer-specific survival for patients with colorectal cancer. Methods Patients with colorectal cancer diagnosed between 1988 and 2006 and those who underwent surgery were retrieved from the Surveillance, Epidemiology, and End Results database and randomly divided into the training (n = 119 797) and validation (n = 119 797) cohorts. Log-rank and multivariate Cox regression analyses were used in our analysis. To find out death from other cancer causes and non-cancer causes, a competing-risks model was used, based on which we integrated these significant prognostic factors into nomograms and subjected the nomograms to bootstrap internal validation and to external validation. Results The 1-, 3-, 5- and 10-year probabilities of overall survival in patients of colorectal cancer after surgery intervention were 83.04, 65.54, 54.79 and 38.62%, respectively. The 1-, 3-, 5- and 10-year cancer-specific survival was 87.36, 73.44, 66.22 and 59.11%, respectively. Nine independent prognostic factors for overall survival and nine independent prognostic factors for cancer specific survival were included to build the nomograms. Internal and external validation CI indexes of overall survival were 0.722 and 0.721, and those of cancer-specific survival were 0.765 and 0.766, which was satisfactory. Conclusions Nomograms for prediction of overall survival and cancer-specific survival of patients with colorectal cancer. Performance of the model was excellent. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

scholarly journals Development and Validation of a Nomogram Based on Nutritional Indicators and Tumor Markers for Prognosis Prediction of Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Haoran Li ◽  
Fang Zhou ◽  
Zhifei Cao ◽  
Yuchen Tang ◽  
Yujie Huang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Markers ◽  
Lymphocyte Count ◽  
Cox Regression ◽  
External Validation ◽  
Training Group ◽  
Ductal Adenocarcinoma ◽  
Validation Group ◽  
Internal Validation ◽  
Cox Regression Analysis

PurposeThis study aimed to develop and validate a nomogram with preoperative nutritional indicators and tumor markers for predicting prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).MethodsWe performed a bicentric, retrospective study including 155 eligible patients with PDAC. Patients were divided into a training group (n = 95), an internal validation group (n = 34), an external validation group (n = 26), and an entire validation group (n = 60). Cox regression analysis was conducted in the training group to identify independent prognostic factors to construct a nomogram for overall survival (OS) prediction. The performance of the nomogram was assessed in validation groups and through comparison with controlling nutritional status (CONUT) and prognostic nutrition index (PNI).ResultsThe least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate Cox regression analysis revealed that serum albumin and lymphocyte count were independent protective factors while CA19-9 and diabetes were independent risk factors. The concordance index (C-index) of the nomogram in the training, internal validation, external validation and entire validation groups were 0.777, 0.769, 0.759 and 0.774 respectively. The areas under curve (AUC) of the nomogram in each group were 0.861, 0.845, 0.773, and 0.814. C-index and AUC of the nomogram were better than those of CONUT and PNI in the training and validation groups. The net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis showed improvement of accuracy of the nomogram in predicting OS and better net benefit in guiding clinical decisions in comparison with CONUT and PNI.ConclusionsThe nomogram incorporating four preoperative nutritional and tumor markers including serum albumin concentration, lymphocyte count, CA19-9 and diabetes mellitus could predict the prognosis more accurately than CONUT and PNI and may serve as a clinical decision support tool to determine what treatment options to choose.

scholarly journals SURG-02. SURVIVAL PREDICTION AFTER NEUROSURGICAL RESECTION OF BRAIN METASTASES: A MACHINE LEARNING APPROACH

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii203-ii203
Author(s):  
Alexander Hulsbergen ◽  
Yu Tung Lo ◽  
Vasileios Kavouridis ◽  
John Phillips ◽  
Timothy Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Metastases ◽  
External Validation ◽  
Superior Performance ◽  
Prognostic Models ◽  
Receiver Operating Curve ◽  
Gradient Boosting ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Adaptive Boosting

Abstract INTRODUCTION Survival prediction in brain metastases (BMs) remains challenging. Current prognostic models have been created and validated almost completely with data from patients receiving radiotherapy only, leaving uncertainty about surgical patients. Therefore, the aim of this study was to build and validate a model predicting 6-month survival after BM resection using different machine learning (ML) algorithms. METHODS An institutional database of 1062 patients who underwent resection for BM was split into a 80:20 training and testing set. Seven different ML algorithms were trained and assessed for performance. Moreover, an ensemble model was created incorporating random forest, adaptive boosting, gradient boosting, and logistic regression algorithms. Five-fold cross validation was used for hyperparameter tuning. Model performance was assessed using area under the receiver-operating curve (AUC) and calibration and was compared against the diagnosis-specific graded prognostic assessment (ds-GPA); the most established prognostic model in BMs. RESULTS The ensemble model showed superior performance with an AUC of 0.81 in the hold-out test set, a calibration slope of 1.14, and a calibration intercept of -0.08, outperforming the ds-GPA (AUC 0.68). Patients were stratified into high-, medium- and low-risk groups for death at 6 months; these strata strongly predicted both 6-months and longitudinal overall survival (p < 0.001). CONCLUSIONS We developed and internally validated an ensemble ML model that accurately predicts 6-month survival after neurosurgical resection for BM, outperforms the most established model in the literature, and allows for meaningful risk stratification. Future efforts should focus on external validation of our model.

scholarly journals Anatomic Subsites and Prognosis of Gastric Signet Ring Cell Carcinoma: A SEER Population-based 1:1 Propensity-matched Study

2021 ◽  
Author(s):  
Yangyang Xie ◽  
Xue Song ◽  
Haimin Jin ◽  
Zhongkai Ni ◽  
Xiaowen Li ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Mortality Risk ◽  
Cox Regression ◽  
Signet Ring Cell Carcinoma ◽  
Signet Ring Cell ◽  
Survival Prediction ◽  
Cancer Specific Survival ◽  
Dismal Prognosis ◽  
Signet Ring ◽  
Ring Cell

Abstract Background: The dismal prognosis of gastric signet ring cell carcinoma (GSRC) is a global problem. The current study is conducted to comprehensively evaluate clinicopathological features and survival outcomes in GSRC patients stratified by anatomic subsites. Then predictive nomograms are constructed and validated to improve the effectiveness of personalized management.Method: The patients diagnosed with GSRC were recruited from the online SEER database. The influence of anatomic subsites on overall survival (OS) and cancer-specific survival (CSS) was evaluated using multivariate Cox regression and Kaplan-Meier analysis. Then we employed propensity score matching (PSM) technique to decrease selection bias and balance patients’ epidemiological factors. Predictive nomograms were constructed and validated.Results: Multivariate Cox regression demonstrated that the patients with overlapping gastric cancer (OGC) suffered the highest mortality risk for OS (HR, 1.29; 95%CI, 1.23-1.36; P<0.001) and CSS (HR, 1.33; 95%CI, 1.28-1.37; P<0.001). Age, TNM stage, tumor localization, tumor size, surgery and chemotherapy presented a highly significant relationship with OS and CSS. Following subgroup and PSM analysis, OGC patients were confirmed to have the worst OS and CSS. Then nomograms predicting 6 months, 12 months and 36 months OS and CSS were constructed. The calibration curves and reveiver operating characteristic curves demonstrated the great performance of the nomograms.Conclusion: We identified anatomic subsites as a predictor of survival in those with GSRC. Patients with OGC suffered the highest mortality risk. The proposed nomograms allowed a relatively accurate survival prediction for GSRC patients.

Multi-Institutional Validation of a New Renal Cancer–Specific Survival Nomogram

2007 ◽  
Vol 25 (11) ◽  
pp. 1316-1322 ◽  
Author(s):  
Pierre I. Karakiewicz ◽  
Alberto Briganti ◽  
Felix K.-H. Chun ◽  
Quoc-Dien Trinh ◽  
Paul Perrotte ◽  
...  
Keyword(s):  
Renal Cancer ◽  
Proportional Hazards ◽  
Predictive Accuracy ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Fuhrman Grade ◽  
Histologic Subtype ◽  
Cancer Specific Mortality

Purpose We tested the hypothesis that the prediction of renal cancer–specific survival can be improved if traditional predictor variables are used within a prognostic nomogram. Patients and Methods Two cohorts of patients treated with either radical or partial nephrectomy for renal cortical tumors were used: one (n = 2,530) for nomogram development and for internal validation (200 bootstrap resamples), and a second (n = 1,422) for external validation. Cox proportional hazards regression analyses modeled the 2002 TNM stages, tumor size, Fuhrman grade, histologic subtype, local symptoms, age, and sex. The accuracy of the nomogram was compared with an established staging scheme. Results Cancer-specific mortality was observed in 598 (23.6%) patients, whereas 200 (7.9%) died as a result of other causes. Follow-up ranged from 0.1 to 286 months (median, 38.8 months). External validation of the nomogram at 1, 2, 5, and 10 years after nephrectomy revealed predictive accuracy of 87.8%, 89.2%, 86.7%, and 88.8%, respectively. Conversely, the alternative staging scheme predicting at 2 and 5 years was less accurate, as evidenced by 86.1% (P = .006) and 83.9% (P = .02) estimates. Conclusion The new nomogram is more contemporary, provides predictions that reach further in time and, compared with its alternative, which predicts at 2 and 5 years, generates 3.1% and 2.8% more accurate predictions, respectively.

A cardiovascular risk score for hypertensive patients previously admitted to hospital

2019 ◽  
Vol 18 (6) ◽  
pp. 492-500 ◽  
Author(s):  
Ana M Martínez-Díaz ◽  
Antonio Palazón-Bru ◽  
David M Folgado-de la Rosa ◽  
Dolores Ramírez-Prado ◽  
Patricia Llópez-Espinós ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Cox Regression ◽  
External Validation ◽  
Arterial Disease ◽  
Internal Validation ◽  
Hypertensive Patients ◽  
One Year

Background: Cardiovascular risk scales in hypertensive populations have limitations for clinical practice. Aims: To develop and internally validate a predictive model to estimate one-year cardiovascular risk for hypertensive patients admitted to hospital. Methods: Cohort study of 303 hypertensive patients admitted through the Emergency Department in a Spanish region in 2015–2017. The main variable was the onset of cardiovascular disease during follow-up. The secondary variables were: gender, age, educational level, family history of cardiovascular disease, Charlson score and its individual conditions, living alone, quality of life, smoking, blood pressure, physical activity and adherence to the Mediterranean diet. A Cox regression model was constructed to predict cardiovascular disease one year after admission. This was then adapted to a points system, externally validated by bootstrapping (discrimination and calibration) and implemented in a mobile application for Android. Results: A total of 93 patients developed cardiovascular disease (30.7%) over a mean period of 1.68 years. The predictors in the points system were: gender, age, myocardial infarction, heart failure, peripheral arterial disease and daily activity (quality of life). The internal validation by bootstrapping was satisfactory. Conclusion: A novel points system was developed to predict short-term cardiovascular disease in hypertensive patients after hospital admission. External validation studies are needed to corroborate the results obtained.

scholarly journals Ferroptosis-Related Long Non-Coding RNA Signature Contributes to the Prediction of Prognosis Outcomes in Head and Neck Squamous Cell Carcinomas

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenru Jiang ◽  
Yingtao Song ◽  
Zhaowei Zhong ◽  
Jili Gao ◽  
Xiaofei Meng
Keyword(s):  
Cell Death ◽  
Programmed Cell Death ◽  
Head And Neck ◽  
Squamous Cell ◽  
Risk Group ◽  
External Validation ◽  
Low Risk ◽  
Survival Prediction ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor, which makes the prognosis prediction challenging. Ferroptosis is an iron-dependent form of non-apoptotic regulated cell death, which could affect cancer development. However, the prognostic value of ferroptosis-related long non-coding RNA (lncRNA) in HNSCC is still limited.Methods: In the current study, we employed the DESeq2 method to characterize the differentially expressed ferroptosis-related genes (FEGs) between cancer and normal samples. Next, the FEG-related lncRNAs (FElncRNAs) were identified using Spearman’s correlation analysis and multiple permutation hypotheses. Subsequently, LASSO and stepwise multivariate Cox regression analyses were undertaken to recognize the prognosis-related FElncRNA signature (PFLS) and risk scores.Results: Herein, we first identified 60 dysregulated FEGs and their co-expressed FElncRNAs in HNSCC. Then, we recognized a set of six FElncRNAs PFLS (SLCO4A1-AS1, C1RL-AS1, PCED1B-AS1, HOXB-AS3, MIR9-3HG, and SFTA1P) for predicting patients’ prognostic risks and survival outcomes. We also assessed the efficiency of PFLS in the test set and an external validation cohort. Further parsing of the tumor immune microenvironment showed the PFLS was closely associated with immune cell infiltration abundances. Notably, the low-risk group of the PFLS showed a higher MHC score and cytolytic activity (CYT) score than the high-risk group, implying the low-risk group may have greater tumor surveillance and killing ability. In addition, we observed that the expression levels of two immune checkpoints (ICPs), i.e., programmed cell death protein 1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1), showed significant associations with patients’ risk score, prompting the role of the PFLS in ICP blockade therapy. Finally, we also constructed a drug–PFLS network to reinforce the clinical utilities of the PFLS.Conclusion: In summary, our study indicated that FElncRNAs played an important role in HNSCC survival prediction. Identification of PFLS will contribute to the development of novel anticancer therapeutic strategies.

scholarly journals Nomograms Predicting Long-Term Overall Survival and Cancer-Specific Survival in Metastatic Hepatocellular Carcinoma Patients

2020 ◽  
Author(s):  
Chi Cui ◽  
Yaru Duan ◽  
Rui Li ◽  
Hua Ye ◽  
Peng Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
Training Group ◽  
Clinicopathological Characteristics ◽  
Validation Group ◽  
Cancer Specific Survival ◽  
Term Survival ◽  
Metastatic Hepatocellular Carcinoma

Abstract Background This study aims to evaluate the clinicopathological characteristics of metastatic hepatocellular carcinoma (HCC) patients and develop nomograms to predict their long-term overall survival (OS) and cancer-specific survival (CSS). Methods Information on metastatic HCC from 2010 to 2015 was retrieved from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute. The metastatic HCC patients were divided into a long-term survival (LTS) group and a short-term survival (STS) group with 1 year selected as the cut-off value. Then, we compared the demographic and clinicopathological features between the two groups. Next, all patients were randomly divided into a training group and validation group at a 7:3 ratio. Univariate and multivariate Cox regression analyses were used to identify potential predictors for OS and CSS in the training group, and nomograms of OS and CSS were established. These predictive models were further validated in the validation group. Results A total of 2163 patients were included in the current study according to the inclusion and exclusion criteria. Patients with characteristics including lower T stage and N stage; treatment with surgery, radiation or chemotherapy; no lung metastasis; and AFP negative status showed better survival. The concordance index (C-index) of the OS nomogram was 0.72 based on 9 variables. The C-index of the CSS nomogram was 0.71 based on 8 variables. Conclusions These nomograms may help clinicians make better treatment recommendations for metastatic HCC patients.

scholarly journals Effect of Aging-Related Genes on the Prognosis of Colon Cancer

2021 ◽  
Author(s):  
Yushu Liu ◽  
Jiantao Gong ◽  
Yanyi Huang ◽  
Qunguang Jiang
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Predictive Ability ◽  
Lasso Regression ◽  
Internal Validation ◽  
Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

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