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.

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.

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 Prognostic Model Constructed Based on Rna Binding Protein to Predict the Prognosis of Patients With Rectal Cancer

2021 ◽  
Author(s):  
Liqiang Zhou ◽  
You Wu ◽  
Shihao Li ◽  
Dengzhong Wu ◽  
Jinliang Wang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Roc Curve ◽  
Prognostic Model ◽  
Clinical Decision Making ◽  
Rna Binding ◽  
Validation Cohort ◽  
Rna Binding Protein ◽  
Clinical Decision ◽  
Clinical Samples ◽  
Training Cohort

Abstract Background: The incidence of rectal cancer in young people is increasing, and there has been a problem of poor prognosis in recent years. Many studies have shown that RNA binding protein (RBP) is related to the progression of various malignant tumors. However, the role of RBPs in rectal cancer is poorly understood. New prognostic models are urgently needed.Materials and methods: In the study, we used the RBPTD database, The Cancer Genome Atlas (TCGA) database and the transcription data information and corresponding clinical information of rectal cancer patients in the Gene Expression Omnibus (GEO) database to screen out RBPs that are differentially expressed in tumor tissues and normal tissues. Subsequently, we analyzed the prognostic value of these RBPs using bioinformatics methods. In order to screen the key RBP in the occurrence of rectal tumors and establish a prognostic risk score model. The use of survival analysis shows that assessing the relationship between key RBPs and the patient's overall survival rate. In the TCGA cohort, the prognostic model was further tested. At the same time, the nomogram of the 6 RBP mRNAs in the TCGA cohort was constructed, and the ROC curve was used for verification. Finally, q-PCR was performed on clinical samples to verify the expression of hub genes.Results: The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good results in the training cohort and validation cohort. The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good survival prediction in both the training cohort and the validation cohort. The constructed nomogram has certain guiding significance for clinical decision-making. In addition, GSEA analysis revealed potential biological functions. The q-PCR verification results showed the consistency with the construction of the prognostic model.Conclusions: We constructed a six RBPs prognostic model and a nomogram to predict the prognosis of patients with rectal cancer, and performed q-PCR expression testing through clinical samples, which may help clinical decision-making.

scholarly journals Validation of parsimonious prognostic models for patients infected with COVID-19

2021 ◽  
Vol 28 (1) ◽  
pp. e100267
Author(s):  
Keerthi Harish ◽  
Ben Zhang ◽  
Peter Stella ◽  
Kevin Hauck ◽  
Marwa M Moussa ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Clinical Decision ◽  
Prognostic Models ◽  
Clinical Prediction ◽  
Inclusion Criteria ◽  
Local Data ◽  
External Data ◽  
Operator Curve

ObjectivesPredictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data.MethodsWe performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020.ResultsMost models failed validation when applied to our institution’s data. Included studies reported an average validation area under the receiver–operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies’ reported AUROC values.DiscussionPublished and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations.ConclusionsClinicians should employ caution when applying models for clinical prediction without careful validation on local data.

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.

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 Unlocking the Potential of Saliva-Based Test to Detect HPV-16-Driven Oropharyngeal Cancer

Cancers ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 473 ◽  
Author(s):  
Kai Dun Tang ◽  
Kurt Baeten ◽  
Liz Kenny ◽  
Ian H. Frazer ◽  
Gert Scheper ◽  
...  
Keyword(s):  
Viral Load ◽  
Oropharyngeal Cancer ◽  
Clinical Decision Making ◽  
Cancer Control ◽  
Staging System ◽  
Clinical Decision ◽  
Tnm Staging ◽  
Hpv 16 ◽  
Eighth Edition ◽  
P16 Expression

The incidence of human papillomavirus (HPV)-positive oropharyngeal cancer (OPC) is rising in high-income countries, including Australia. Increasing evidence suggests that accurate HPV testing is pivotal for clinical decision making and treatment planning in these patients. Recently, the eighth edition of the American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) tumor–node–metastasis (TNM) staging system for OPC (based on the p16INK4a (p16) status) was proposed and has been implemented. However, the applicability of this new staging system is still far from clear. In our study, n = 127 OPC patients from Queensland, Australia were recruited, and the tumor p16 expression in these patients was examined using immunohistochemical (IHC) analysis. HPV-16 genotyping, viral load, and physical status (episomal versus integrated) in the saliva samples of OPC patients were determined using the qPCR method. A good inter-rater agreement (k = 0.612) was found between tumor p16 expression and oral HPV-16 infection in OPC. Importantly, according to the eighth edition staging system, HPV-16 DNA viral load (>10 copies/50 ng) was significantly associated with the advanced stages of OPC. In concordance with previous studies, a mixed HPV-16 form (partially or fully integrated) was predominately found in OPC patients. Taken together, our data support HPV-16 detection in saliva as a screening biomarker to identify people within the community who are at risk of developing OPC.

scholarly journals Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Yi Jiang ◽  
Jingjing Sun ◽  
Yuwei Xia ◽  
Yan Cheng ◽  
Linjun Xie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Multivariable Analysis ◽  
Tumor Diameter ◽  
Event Free Survival ◽  
Free Survival ◽  
Training Cohort ◽  
Radiomics Signature

Objective: To explore a CT-based radiomics model for preoperative prediction of event-free survival (EFS) in patients with hepatoblastoma and to compare its performance with that of a clinicopathologic model.Patients and Methods: Eighty-eight patients with histologically confirmed hepatoblastoma (mean age: 2.28 ± 2.72 years) were recruited from two institutions between 2002 and 2019 for this retrospective study. They were divided into a training cohort (65 patients from institution A) and a validation cohort (23 patients from institution B). Radiomics features were extracted manually from pretreatment CT images in the portal venous (PV) phase. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to construct a “radiomics signature” and radiomics score (Rad-score) for EFS prediction. Then, a nomogram incorporating the Rad-score, updated staging system, and significant variables of clinicopathologic risk (age, alpha-fetoprotein (AFP) level, histology subtype, tumor diameter) as the radiomic model, clinicopathologic model, and combined clinicopathologic-radiomic model were built for EFS estimation in the training cohort, the performance of which was assessed in an external-validation cohort with respect to clinical usefulness, discrimination, and calibration.Results: Nine survival-relevant features were selected for a radiomics signature and Rad-score building. Multivariable analysis revealed that histology subtype (P = 0.01), PV (P = 0.001) invasion, and metastasis (P = 0.047) were independent risk factors of EFS. Patients were divided into low- and high-risk groups based on the Rad-score with a cutoff of 0.08 according to survival outcome. The radiomics signature-incorporated nomogram showed good performance (P &lt; 0.001) for EFS estimation (C-Index: 0.810; 95% CI: 0.738–0.882), which was comparable with that of the clinicopathological model for EFS estimation (C-Index: 0.81 vs. 0.85). The radiomics-based nomogram failed to show incremental prognostic value compared with that using the clinicopathologic model. The combined model (radiomics signature plus clinicopathologic parameters) showed significant improvement in the discriminatory accuracy, along with good calibration and greater net clinical benefit, of EFS (C-Index: 0.88; 95% CI: 0.829–0.933).Conclusion: The radiomics signature can be used as a prognostic indicator for EFS in patients with hepatoblastoma. A combination of the radiomics signature and clinicopathologic risk factors showed better performance in terms of EFS prediction in patients with hepatoblastoma, which enabled precise clinical decision-making.

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