scholarly journals Construction and External Validation of a Nomogram for Predicting Survival of Intrahepatic Cholangiocarcinoma Patients Without Lymph Node and Distant Metastasis

2021 ◽  
Author(s):  
Chuang Jiang ◽  
Fei Teng ◽  
Yunyou Tang ◽  
Ziqi Zhang ◽  
Yimin Chen ◽  
...  
Keyword(s):  
Lymph Node ◽  
Intrahepatic Cholangiocarcinoma ◽  
Validation Cohort ◽  
External Validation ◽  
Staging System ◽  
Tnm Staging ◽  
Internal Validation ◽  
West China Hospital ◽  
Training Cohort ◽  
West China

Abstract BackgroundThe purpose of this study was to construct and external validate a nomogram for predicting overall survival(OS) in intrahepatic cholangiocarcinoma (ICC) patients classified as N0M0 according to the 7th edition of American Joint Committee on Cancer (AJCC) TNM staging system.Methods:812 ICC patients without distant and lymph node metastasis between 2011 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database, then randomly assigned to the training cohort(n=648) or internal validation cohort(n=164), external validation cohort consisted of 136 ICC patients with N0M0 stage treated in West China Hospital of Sichuan University from 2013 to 2015. The precision of the nomogram was validated internally using SEER validation cohort and externally using the patients’ data of West China Hospital. Results :The nomogram was established to predict 1-year, 3-year and 5-year OS and the calibration curve showed nomogram prediction performance was in good agreement with the actual results. The C‑index of the nomogram was 0.750(95% CI:0.731-0.769) in the training cohort, and the internal and external validated C-indexes were 0.803(95% CI:0.783-0.823) and 0.681(95% CI:0.524-0.838), respectively. In the training, internal and external validation cohort, the 1-year, 3‑year and 5‑year AUCs were (0.772,0.809,0.798),(0.896,0.868,0.896) and (0.673,0.786,0.886), respectively.Conclusions This nomogram has an excellent predictive effect on the 1- ,3-, 5-year OS of ICC patients with stage N0M0 and guide the optimal treatment for these type of patients.

scholarly journals Development and validation of nomogram combining serum biomarker for predicting survival in patients with resected rectal cancer

2019 ◽  
Vol 39 (11) ◽  
Author(s):  
Shaonan Fan ◽  
Ting Li ◽  
Ping Zhou ◽  
Qiliang Peng ◽  
Yaqun Zhu
Keyword(s):  
Rectal Cancer ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Staging System ◽  
Clinical Decision ◽  
Tnm Staging ◽  
P Value ◽  
Training Cohort ◽  
American Joint Committee

Abstract Purpose: Nomogram is a widely used tool that precisely predicts individualized cancer prognoses. We aimed to develop and validate a reliable nomogram including serum tumor biomarkers to predict individual overall survival (OS) for patients with resected rectal cancer (RC) and compare the predictive value with the American Joint Committee on Cancer (AJCC) stages. Patients and methods: We analyzed 520 patients who were diagnosed with non-metastatic rectal cancer as training cohort. External validation was performed in a cohort of 11851 patients from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified and integrated to build a nomogram using the Cox proportional hazard regression model. The nomogram was evaluated by Harrell’s concordance index (C-index) and calibration plots in both training and validation cohort. Results: The calibration curves for probability of 1-, 3-, and 5-year OS in both cohorts showed favorable accordance between the nomogram prediction and the actual observation. The C-indices of the nomograms to predict OS were 0.71 in training cohort and 0.69 in the SEER cohort, which were higher than that of the seventh edition American Joint Committee on Cancer TNM staging system for predicting OS (training cohort, 0.71 vs. 0.58, respectively; P-value < 0.001; validation cohort, 0.69 vs. 0.57, respectively; P-value < 0.001). Conclusion: We developed and validated a novel nomogram based on CEA and other factors for predicting OS in patients with resected RC, which could assist clinical decision making and improvement of prognosis prediction for individual RC patients after surgery.

Establishment of a Nomogram by Integrating Molecular Markers and Tumor-Node-Metastasis Staging System for Predicting the Prognosis of Hepatocellular Carcinoma

2018 ◽  
Vol 36 (5) ◽  
pp. 426-432 ◽  
Author(s):  
Xi-Tai Huang ◽  
Liu-Hua Chen ◽  
Chen-Song Huang ◽  
Jian-Hui Li ◽  
Jian-Peng Cai ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Validation Cohort ◽  
External Validation ◽  
Staging System ◽  
Tnm Staging ◽  
Hazard Ratio ◽  
Tumor Node Metastasis ◽  
Training Cohort ◽  
Tnm Staging System

Aims: This study aimed to develop a valuable nomogram by integrating molecular markers and tumor-node-metastasis (TNM) staging system for predicting the long-term outcome of patients with hepatocellular carcinoma (HCC). Methods: The gene expression profiles of HCC patients undergoing liver resection were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. One hundred and ninety-nine patients from TCGA and 94 patients from GEO were selected to be part of the training cohort and validation cohort respectively. Univariate and multivariate cox analyses were performed to identify genes with independent prognostic values for overall survival (OS) of HCC patients in training cohort. Risk score was calculated based on the coefficients and Z-score of 3 genes for each patient. The nomogram was developed based on the risk score and TNM staging system. Discrimination and predictive accuracy of the nomogram were measured by using the concordance index (C-index) and calibration curve. The efficacy of the nomogram was tested in the external validation cohort. Results: Univariate and multivariate cox analyses revealed that EXT2 (p = 0.035, hazard ratio 13.412), ETV5 (p = 0.010, hazard ratio 4.325), and CHODL (p < 0.001, hazard ratio 6.286) were independent prognostic factors and chosen for further nomogram establishment. The C-index of the nomogram for predicting the OS in the training cohort was superior to that of the TNM staging system (0.77 vs. 0.64, p < 0.01). The calibration curve of predicted 1-, 3-, and 5-year OS showed satisfactory accuracy. The external validation cohort showed good performance of comprehensive nomogram as well. Conclusion: The novel nomogram by integrating the molecular markers and TNM staging system has better performance in predicting long-term prognosis in HCC patients than the TNM staging system alone.

scholarly journals A Novel Clinical Nomogram for Predicting Lymph Node Metastasis in Ovarian Cancer: A SEER Analysis and External Validation in a Tertiary Center

2021 ◽  
Author(s):  
Guiping Zhang ◽  
Wei Ren
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Validation Cohort ◽  
External Validation ◽  
Internal Validation ◽  
Training Cohort ◽  
Preoperative Serum ◽  
Histology Grade ◽  
Serum Ca125 ◽  
Serum Ca125 Level

Abstract Introduction The aim of the study is to investigate the risk factors for developing lymph node metastases (LNM) in cases diagnosed as a presumed early-stage ovarian carcinoma (OC). Methodology Information of patients who had been diagnosed as OC in 2018 was obtained from the SEER database. We enrolled 104 OC patients in General Hospital of Northern Theatre Command for external validation. A logistic regression was conducted to determine the independent predictors for LNM, which were used for establishing a nomogram. In order to evaluate the reliability of nomogram, we applied a receiver operating characteristic curve (ROC) analysis, calibration curves and plotted decision curves. Results We found that age(≥70, OR=0.544, p=0.022), histology type (Mucinous carcinoma, OR=0.390, p=0.001; Endometrioid carcinoma, OR=7.946, p=0.053; Others, OR=2.400, p=0.040), histology grade (Grade II, OR=2.423, p=0.028; Grade III, OR=1.982, p=0.152; Grade IV, OR=1.594, p=0.063) and preoperative serum CA125 level (positive, OR=2.236, p=0.001) were all significant predictors of LNM. The AUC of the model training cohort, internal validation cohort, and external validation cohort were 0.78, 0.79 and 0.76 respectively. The calibration curves showed that the predicted outcome fitted well to the observed outcome in the training cohort (p=0.825) internal validation cohort (p=0.503), and external validation cohort (p=0.108). The decision curves showed the nomogram had more benefits than the All or None scheme if the threshold probability is >50% and <100% in training cohort and internal validation cohort, >30% and <90% in the external validation cohort. Conclusion The multivariate logistic regression showed that age, histology type, histology grade and preoperative serum CA125 level were all significant predictors of LNM. The nomogram established using the above variables had great performance for clinical applying.

scholarly journals Predicting Survival for Nasopharyngeal Carcinoma: Development and Validation of Nomograms With Routine Hematological Biomarkers Based on the 8th Edition of AJCC/UICC Staging System

2020 ◽  
Author(s):  
Yu Liang ◽  
Kaihua Chen ◽  
Jie Yang ◽  
Jing Zhang ◽  
Rurong Peng ◽  
...  
Keyword(s):  
Validation Cohort ◽  
External Validation ◽  
Progression Free Survival ◽  
Staging System ◽  
Tnm Staging ◽  
Prognostic Role ◽  
Tnm System ◽  
Ebv Dna ◽  
8Th Edition

Abstract BackgroundThe 8th edition of AJCC/UICC TNM staging system (TNM system) and the previous nomograms have limitations, therefore we aimed to develop and validate nomograms incorporating routine hematological biomarkers with or without EBV DNA for overall survival (OS) and progression-free survival (PFS). We also evaluated the prognostic role of EBV DNA.Material and Methods1203 patients at our hospital from 2013 to 2016 were retrospectively reviewed and divided into two parts (922 patients for primary cohort and 281 for validation cohort). Nomograms (nomogram with or without EBV DNA) were developed and compared with other models (TNM system alone, TNM system with EBV DNA), via comparison the prognostic role of EBV DNA was evaluated. Internal and external validation were performed. Risk stratification were conducted with recursive partitioning analysis.ResultsThe nomograms with EBV DNA for OS and PFS included sex, age, T category, N category, EBV DNA, albumin, neutrophil to lymphocyte ratio and lactate dehydrogenase. The nomograms without EBV DNA for OS and PFS included the same variables but without EBV DNA. The C-index for nomogram with EBV DNA was 0.715 for OS and 0.705 for PFS. For nomogram without EBV DNA, it was 0.709 and 0.700, respectively. It was 0.639 and 0.636 for TNM system alone and 0.648, 0.646 respectively for TNM system with EBV DNA. The nomograms with or without EBV DNA had better performance than both the TNM system alone and TNM system with EBV DNA, while the TNM system with EBV DNA were better than TNM system alone. The validation cohort indicates great applicability of nomograms. The patients were stratified into 4 risk groups.ConclusionThe nomograms with or without EBV DNA provide better prognostication than the TNM system and also the TNM system with EBV DNA. EBV DNA is valuable in predicting survival, but it is not suggested to incorporate EBV DNA alone to TNM system.

scholarly journals Derivation and Validation of a Prognostic Scoring Model Based on Clinical and Pathological Features for Risk Stratification in Oral Squamous Cell Carcinoma Patients: A Retrospective Multicenter Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiaying Zhou ◽  
Huan Li ◽  
Bin Cheng ◽  
Ruoyan Cao ◽  
Fengyuan Zou ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Validation Cohort ◽  
External Validation ◽  
Staging System ◽  
Tnm Staging ◽  
Pathological Features ◽  
Scoring Model ◽  
Tnm Staging System ◽  
Pathological Model ◽  
Clinical And Pathological Features

ObjectiveTo develop and validate a simple-to-use prognostic scoring model based on clinical and pathological features which can predict overall survival (OS) of patients with oral squamous cell carcinoma (OSCC) and facilitate personalized treatment planning.Materials and MethodsOSCC patients (n = 404) from a public hospital were divided into a training cohort (n = 282) and an internal validation cohort (n = 122). A total of 12 clinical and pathological features were included in Kaplan–Meier analysis to identify the factors associated with OS. Multivariable Cox proportional hazards regression analysis was performed to further identify important variables and establish prognostic models. Nomogram was generated to predict the individual’s 1-, 3- and 5-year OS rates. The performance of the prognostic scoring model was compared with that of the pathological one and the AJCC TNM staging system by the receiver operating characteristic curve (ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA). Patients were classified into high- and low-risk groups according to the risk scores of the nomogram. The nomogram-illustrated model was independently tested in an external validation cohort of 95 patients.ResultsFour significant variables (physical examination-tumor size, imaging examination-tumor size, pathological nodal involvement stage, and histologic grade) were included into the nomogram-illustrated model (clinical–pathological model). The area under the ROC curve (AUC) of the clinical–pathological model was 0.687, 0.719, and 0.722 for 1-, 3- and 5-year survival, respectively, which was superior to that of the pathological model (AUC = 0.649, 0.707, 0.717, respectively) and AJCC TNM staging system (AUC = 0.628, 0.668, 0.677, respectively). The clinical–pathological model exhibited improved discriminative power compared with pathological model and AJCC TNM staging system (C-index = 0.755, 0.702, 0.642, respectively) in the external validation cohort. The calibration curves and DCA also displayed excellent predictive performances.ConclusionThis clinical and pathological feature based prognostic scoring model showed better predictive ability compared with the pathological one, which would be a useful tool of personalized accurate risk stratification and precision therapy planning for OSCC patients.

scholarly journals A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Liu ◽  
Xiaoyuan Liang ◽  
Lingxiang Ruan ◽  
Sheng Yan
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Gallbladder Cancer ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Clinical Variables ◽  
Node Metastasis ◽  
Preoperative Prediction

ObjectivesThe aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).MethodsA total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.ResultsThe clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.ConclusionThe findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

P0616REAL-TIME CLINICAL DECISION SUPPORT TO PREDICT AND MANAGE IN-HOSPITAL ACUTE KIDNEY INJURY UTILIZING RECURRENT NEURAL NETWORK

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Hea Eun Kim ◽  
Hyeonsik Yang ◽  
Sejoong Kim ◽  
Kipyo Kim
Keyword(s):  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Vital Signs ◽  
Clinical Decision ◽  
Internal Validation ◽  
Training Cohort ◽  
National University ◽  
Seoul National University ◽  
Dynamic Variables

Abstract Background and Aims Rapidly Increasing electronic health record (EHR) data and recent development of machine learning methods offers the possibilities of improvement in quality of care in clinical practice. Machine learning can incorporate huge amount of features into the model, and enable non-linear algorithms with great performance. Previously published AKI prediction models have simple design without real-time assessment. Major risk factors in in-hospital AKI include use of various nephrotoxins, repeatedly measured laboratory findings, and vital signs, which are dynamic variables rather than static. Given that recurrent neural network (RNN) is a powerful tool to handle the sequential data, using RNN method in the prediction model is a promising approach. Therefore, in the present study, we proposed a RNN-based prediction model with external validation for in-hospital AKI and aimed to provide a framework to link the developed model with clinical decision supports. Method Study populations were all patients aged ≥ 18 years and hospitalized more than a week at Seoul National University Bundang Hospital (SNUBH) from 2013 to 2017 (training cohort) and at Seoul National University Hospital (SNUH) in 2017 (validation cohort). All demographics, laboratory values, vital signs, and clinical conditions were obtained from the EHR of each hospital. A total of 102 variables included in the model. Each variable falls into two categories: static and dynamic variable; static variable was time-invariant values during hospitalization, and dynamic variables were daily-updated values. Baseline creatinine was determined by searching the minimum serum Cr level within 2 weeks before admission. We developed two different models (model 1 and model 2) using RNN algorithms. The outcome for model 1 was the occurrence of AKI within 7 days from the present. In model 2, we constructed the prediction model of the trajectory of Cr values after 24 hours, 48 hours, and 72 hours, using available Cr values from 7 days ago to the present. Internal validation was performed by 5-fold cross validation using the training set (SNUBH), and then external validation was done using test set (SNUH). Results A total of 40,552 patients in training cohort and 4,000 patients in external validation cohort (test cohort) were included in the study. The mean age of participants was 62.2 years in training cohort and 58.7 years in test cohort. Baseline eGFR was 93.8 ± 40.4 ml/min/1.73m2 in training cohort and 88.4 ± 23.2 ml/min/1.73m2 in test cohort. In model 1 for the prediction of AKI occurrence within 7 days, the area under the curve was 0.93 (sensitivity 0.90, specificity 0.96) in internal validation, and 0.83 (sensitivity 0.83, specificity 0.82) in external validation. The model 2 predicted the creatinine trajectory within 3 days accurately; root mean square error was 0.1 in training cohort and 0.3 in test cohort. To support the clinical decision for AKI manage, we estimated the predicted trajectories of future creatinine levels after renal insult removal, such as nephrotoxic drugs, based on the established model 2. Conclusion We developed and validated a real-time AKI prediction model using RNN algorithms. This model showed high performance and can accurately visualize future creatinine trajectories. In addition, the model can provide the information about modifiable factors in patients with high risk of AKI.

scholarly journals A Novel Preoperative Inflammation Score System Established for Postoperative Prognosis Predicting of Intrahepatic Cholangiocarcinoma

2021 ◽  
Author(s):  
Jun Fu ◽  
Qinjunjie Chen ◽  
Zisen Lai ◽  
Kongying Lin ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Risk Factor ◽  
Intrahepatic Cholangiocarcinoma ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Validation Cohort ◽  
Primary Liver Cancer ◽  
Tnm Stage ◽  
Internal Validation ◽  
Training Cohort ◽  
Inflammation Score

Abstract BackgroundInflammation has been implicated in tumorigenesis and has been reported as an important prognostic factor in cancers. In this study, we aimed to develop and validate a novel inflammation score (IFS) system based on 12 inflammatory markers and explore its impact on intrahepatic cholangiocarcinoma (ICC) survival after hepatectomy.MethodsClinical data of 446 ICC patients underwent surgical treatment were collected from the Primary Liver Cancer Big Data, and then served as a training cohort to establish the IFS. Furthermore, an internal validation cohort including 175 patients was used as internal validation cohort of the IFS. A survival tree analysis was used to divide ICC patients into three groups (low-, median-, and high- IFS-score groups) according to different IFS values. Kaplan-Meier (KM) curves were used to compare the overall survival (OS) and recurrence-free survival (RFS) rates among three different groups. Cox regression analyses were applied to explore the independent risk factors influencing OS and RFS.ResultsIn the training cohort, 149 patients were in the low-IFS-score group, 187 in the median-IFS-score group, and 110 in the high-IFS-score group. KM curves showed that the high-IFS-score group had worse OS and RFS rates than those of the low- and median-IFS-score groups (P<0.001) in both the training and validation cohorts. Moreover, multivariable Cox analyses identified high IFS as an independent risk factor for OS and RFS in the training cohort. The area under the curve values for OS prediction of IFS were 0.703 and 0.664 in the training and validation cohorts, respectively, which were higher than those of the AJCC 7th edition TNM stage, AJCC 8th edition TNM stage, and the Child-Pugh score. ConclusionsOur results revealed IFS was an independent risk factor for OS and RFS in patients with ICC after hepatectomy and could serve as an effective prognostic prediction system in daily clinical practice.

scholarly journals Development and validation of a nomogram for preoperative prediction of lymph node metastasis in early gastric cancer

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xiao-Yi Yin ◽  
Tao Pang ◽  
Yu Liu ◽  
Hang-Tian Cui ◽  
Tian-Hang Luo ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Gastric Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Node Metastasis ◽  
Gastric Cancer Patients

Abstract Background The status of lymph nodes in early gastric cancer is critical to make further clinical treatment decision, but the prediction of lymph node metastasis remains difficult before operation. This study aimed to develop a nomogram that contained preoperative factors to predict lymph node metastasis in early gastric cancer patients. Methods This study analyzed the clinicopathologic features of 823 early gastric cancer patients who underwent gastrectomy retrospectively, among which 596 patients were recruited in the training cohort and 227 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated in and presented with a nomogram. And internal and external validation curves were plotted to evaluate the discrimination of the nomogram. Results Totally, six independent predictors, including the tumor size, macroscopic features, histology differentiation, P53, carbohydrate antigen 19-9, and computed tomography-reported lymph node status, were enrolled in the nomogram. Both the internal validation in the training cohort and the external validation in the validation cohort showed the nomogram had good discriminations, with a C-index of 0.82 (95%CI, 0.78 to 0.86) and 0.77 (95%CI, 0.60 to 0.94) respectively. Conclusions Our study developed a new nomogram which contained the most common and significant preoperative risk factors for lymph node metastasis in patients with early gastric cancer. The nomogram can identify early gastric cancer patients with the high probability of lymph node metastasis and help clinicians make more appropriate decisions in clinical practice.

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