scholarly journals Integrated analysis of serum lipid profile for predicting clinical outcomes of patients with malignant biliary tumor

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lejia Sun ◽  
Xin Ji ◽  
Dongyue Wang ◽  
Ai Guan ◽  
Yao Xiao ◽  
...  
Keyword(s):  
Serum Lipids ◽  
Predictive Ability ◽  
Staging System ◽  
Tnm Staging ◽  
Integrated Analysis ◽  
Survival Prediction ◽  
Related Factors ◽  
Tnm System ◽  
Tnm Staging System ◽  
Lipid Parameters

Abstract Background Serum lipids were reported to be the prognostic factors of various cancers, but their prognostic value in malignant biliary tumor (MBT) patients remains unclear. Thus we aim to assess and compare prognosis values of different serum lipids, and construct a novel prognostic nomogram based on serum lipids. Methods Patients with a confirmed diagnosis of MBT at our institute from 2003 to 2017 were retrospectively reviewed. Prognosis-related factors were identified via univariate and multivariate Cox regression analyses. Then the novel prognostic nomogram and a 3-tier staging system were constructed based on these factors and further compared to the TNM staging system. Results A total of 368 patients were included in this study. Seven optimal survival-related factors—TC/HDL >  10.08, apolipoprotein B >  0.9 g/L, lipoprotein> 72 mg/L, lymph node metastasis, radical cure, CA199 > 37 U/mL, and tumor differentiation —were included to construct the prognostic nomogram. The C-indexes in training and validation sets were 0.738 and 0.721, respectively. Besides, ROC curves, calibration plots, and decision curve analysis all suggested favorable discrimination and predictive ability. The nomogram also performed better predictive ability than the TNM system and nomogram without lipid parameters. And the staging system based on nomogram also presented better discriminative ability than TNM system (P < 0.001). Conclusions The promising prognostic nomogram based on lipid parameters provided an intuitive method for performing survival prediction and facilitating individualized treatment and was a great complement to the TNM staging system in predicting overall survival.

Rhabdomyosarcoma: the Stanford experience using a TNM staging system.

1986 ◽  
Vol 4 (3) ◽  
pp. 370-378 ◽  
Author(s):  
T J Pedrick ◽  
S S Donaldson ◽  
R S Cox
Keyword(s):  
Lymph Nodes ◽  
Staging System ◽  
Tnm Staging ◽  
Actuarial Survival ◽  
Entire Study ◽  
Histologic Subtype ◽  
Tnm System ◽  
T Stage ◽  
Tnm Staging System ◽  
Extent Of Disease

Seventy-four patients with rhabdomyosarcoma were initially staged according to the Intergroup Rhabdomyosarcoma Study (IRS) grouping classification and then retrospectively using a TNM staging system based on the initial clinical extent of disease. The TNM system includes T1, tumor confined to site or organ of origin; T2, regional extension beyond the site of origin; N0, normal lymph nodes; N1, lymph nodes containing tumor; M0, no evidence of metastases; and M1, distant metastases. All patients received combination chemotherapy, and more than 90% received radiation therapy as part of their initial treatment program with curative intent. Fifty-three of 74 patients (72%) were group III according to the IRS system, indicating unresectable or gross residual tumor. A more uniform distribution was achieved using the TNM system. Freedom from relapse (FFR) was 43% and the actuarial survival rate was 47% for the entire study group at 10 years. All but one relapse occurred within 3 years of initial diagnosis, and only three of 38 relapsed patients were salvaged. All TNM stage I patients are surviving disease free. Among patients having stages II, III, and IV disease by the TNM system, FFR was 53%, 26%, and 11%, and the survival rates were 47%, 36%, and 33%, respectively. Thirty-two of 74 patients (43%) had evidence of lymph node involvement at presentation, and 28 (88%) of these had primary lesions that extended beyond the site of origin (T2 primary). Histologic subtype and primary site had little impact on outcome in a multivariate analysis, and T stage was identified as the single most significant covariate correlated with survival; a model composed of both T stage and M stage was the best one for predicting relapse. The presented data support a study using a prospectively assigned TNM staging system based on the initial clinical extent of disease for use in future therapeutic trials.

scholarly journals A Model for the Prediction of Survival in Patients With Upper Tract Urothelial Carcinoma After Surgery

Dose-Response ◽  
2019 ◽  
Vol 17 (4) ◽  
pp. 155932581988287
Author(s):  
Guang-lin Zhang ◽  
Wei Zhou
Keyword(s):  
Urothelial Carcinoma ◽  
Predictive Ability ◽  
Staging System ◽  
Tnm Staging ◽  
Upper Tract Urothelial Carcinoma ◽  
Cancer Specific Survival ◽  
Upper Tract ◽  
Tnm Staging System ◽  
Prediction Of Survival ◽  
Validation Set

Objective: We aimed to formulate and validate prognostic nomograms that can be used to predict the prognosis of patients with upper tract urothelial carcinoma (UTUC). Methods: By consulting the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients who were surgically treated for UTUC between 2004 and 2013. Variables were analyzed in both univariate and multivariate analyses. Nomograms were constructed based on independent prognostic factors. The prognostic nomogram models were established and validated internally and externally to determine their ability to predict the survival of patients with UTUC. Results: A total of 4990 patients were collected and enrolled in our analyses. Of these, 3327 patients were assigned to the training set and 1663 to the validation set. Nomograms were effectively applied to predict the 3- and 5-year survivals of patients with UTUC after surgery. The nomograms exhibited better accuracy for predicting overall survival (OS) and cancer-specific survival (CSS) than the tumor-node-metastasis (TNM) staging system and the SEER stage in both the training and validation sets. Calibration curves indicated that the nomograms exhibited high correlation to actual observed results for both OS and CSS. Conclusions: The nomogram models showed stronger predictive ability than the TNM staging system and the SEER stage. Precise estimates of the prognosis of UTUC might help doctors to make better treatment decisions.

scholarly journals Immunoscore As A Predictor Of Disease Recurrences And Patients’ Survival in Colon Cancer : A Clinicopathologic Study.

2020 ◽  
Vol 8 (E) ◽  
pp. 143-149
Author(s):  
Reham Shehab El Nemr Esmail ◽  
Amr Kamal ◽  
Marwa Shabana
Keyword(s):  
Colon Cancer ◽  
Sensitivity And Specificity ◽  
Staging System ◽  
Good Prognosis ◽  
Tnm Staging ◽  
Image Analysis System ◽  
Predictive Values ◽  
Tnm System ◽  
Clinicopathologic Study ◽  
Tnm Staging System

BACKGROUND: For years, the American Joint Committee of Cancer/International Union against Cancer TNM staging system was the only accepted staging system for colorectal cancer. Different studies highlighted limitations in this staging system with the need to another staging approach that takes into consideration the individual patient immune response. Recently, the immunoscore was introduced; however, no accurate data regarding its sensitivity and specificity over the routinely used TNM staging system. AIM: We aimed to provide definite sensitivity, septicity, and predictive values for both IS and TNM staging system in prognosis prediction, as evidence-based statistical documentation of its validity to clinical use. METHODS: Fifty-three slides of colon cancer cases were stained for CD3 and CD8 immunohistochemical stains. The density of the stained cells was measured used an image analysis system in the core of the tumor and invasive margin. Immunoscore was calculated and results were compared with TNM in the recurrence-free survival of the patients. The sensitivity and specificity for each test were calculated. RESULTS: High IS was correlated with a good prognosis in the studied cases. IS sensitivity reached 85.7% compared to 28.6% in TNM staging system and the specificity was 78.1% compared to 37.5% in TNM system. CONCLUSION: IS is a promising prognostic estimation tool in colon cancer with better sensitivity and specificity than TNM staging system. The routine use of IS is now becoming a mandatory step.

scholarly journals Predicting Renal Cancer Recurrence: Defining Limitations of Existing Prognostic Models With Prospective Trial-Based Validation

2019 ◽  
Vol 37 (23) ◽  
pp. 2062-2071 ◽  
Author(s):  
Andres F. Correa ◽  
Opeyemi Jegede ◽  
Naomi B. Haas ◽  
Keith T. Flaherty ◽  
Michael R. Pins ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Practice ◽  
Prediction Models ◽  
Locally Advanced ◽  
Predictive Ability ◽  
Staging System ◽  
Tnm Staging ◽  
Prognostic Models ◽  
Adjuvant Trial ◽  
Tnm Staging System

PURPOSE To validate currently used recurrence prediction models for renal cell carcinoma (RCC) by using prospective data from the ASSURE (ECOG-ACRIN E2805; Adjuvant Sorafenib or Sunitinib for Unfavorable Renal Carcinoma) adjuvant trial. PATIENTS AND METHODS Eight RCC recurrence models (University of California at Los Angeles Integrated Staging System [UISS]; Stage, Size, Grade, and Necrosis [SSIGN]; Leibovich; Kattan; Memorial Sloan Kettering Cancer Center [MSKCC]; Yaycioglu; Karakiewicz; and Cindolo) were selected on the basis of their use in clinical practice and clinical trial designs. These models along with the TNM staging system were validated using 1,647 patients with resected localized high-grade or locally advanced disease (≥ pT1b grade 3 and 4/pTanyN1Mo) from the ASSURE cohort. The predictive performance of the model was quantified by assessing its discriminatory and calibration abilities. RESULTS Prospective validation of predictive and prognostic models for localized RCC showed a substantial decrease in each of the predictive abilities of the model compared with their original and externally validated discriminatory estimates. Among the models, the SSIGN score performed best (0.688; 95% CI, 0.686 to 0.689), and the UISS model performed worst (0.556; 95% CI, 0.555 to 0.557). Compared with the 2002 TNM staging system (C-index, 0.60), most models only marginally outperformed standard staging. Importantly, all models, including TNM, demonstrated statistically significant variability in their predictive ability over time and were most useful within the first 2 years after diagnosis. CONCLUSION In RCC, as in many other solid malignancies, clinicians rely on retrospective prediction tools to guide patient care and clinical trial selection and largely overestimate their predictive abilities. We used prospective collected adjuvant trial data to validate existing RCC prediction models and demonstrate a sharp decrease in the predictive ability of all models compared with their previous retrospective validations. Accordingly, we recommend prospective validation of any predictive model before implementing it into clinical practice and clinical trial design.

scholarly journals Evaluation of the Eighth Edition of the American Joint Committee on Cancer TNM Staging System for Gastric Cancer: An Analysis of 7371 Patients in the SEER Database

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Long-Long Cao ◽  
Jun Lu ◽  
Ping Li ◽  
Jian-Wei Xie ◽  
Jia-Bin Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Survival Rates ◽  
Stage Iv ◽  
Staging System ◽  
Tnm Staging ◽  
Stage Migration ◽  
Seer Database ◽  
Tnm System ◽  
American Joint Committee ◽  
Tnm Staging System

Objective. To investigate the validity of the 8thedition of the American Joint Committee on Cancer (AJCC) TNM staging system for gastric cancer.Methods. The clinicopathologic data of 7371 patients who were diagnosed with gastric cancer and had 16 or more involved lymph nodes (LNs) were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and retrospectively reviewed.Results. Stage migration occurred primarily during stage III between the 7thand 8thedition TNM staging systems. Stages IIIB and IIIC in the 7thedition staging system were divided in the 8thedition and had obvious differences in survival rates (bothP<0.001). The 8thedition TNM stages IIIC and IV showed similar survival rates (P=0.101). The prognosis of patients with T4aN3bM0 was not different from that of patients with TxNxM1 (P=0.433), while the prognosis of patients with T4bN3bM0 was significantly poorer than that of patients with TxNxM1 (P=0.008). A revised TNM system with both T4aN3bM0 and T4bN3bM0 incorporated into stage IV was proposed. Multivariable regression analysis showed that the revised TNM system, but not the 7thand 8theditions, was an independent factor for disease-specific survival (DSS) in the third step of the analysis. Further analyses revealed that the revised TNM system had superior discriminatory ability to the 8thedition staging system, which was also an improvement over the 7thedition staging system.Conclusion. The 8thedition of the AJCC TNM staging system is superior to the 7thedition for predicting the DSS rates of gastric cancer patients. However, for better prognostic stratification, it might be more suitable for T4aN3bM0/T4bN3bM0 to be incorporated into stage IV in the 8thedition TNM staging system.

Prediction of gastric cancer survival after gastrectomy using nomogram from 10,621 patients: Developed and validated using international databases.

2013 ◽  
Vol 31 (4_suppl) ◽  
pp. 66-66
Author(s):  
Yanghee Woo ◽  
Woo Jin Hyung ◽  
Ki Jun Song ◽  
Yanfeng Hu ◽  
Naoki Okumura ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Staging System ◽  
Tnm Staging ◽  
Patient Specific ◽  
Data Sets ◽  
Data Set ◽  
Tnm System ◽  
Number Of Patients ◽  
Tnm Staging System

66 Background: Patient-specific prognosis for gastric cancer is difficult to determine. Internationally accepted AJCC TNM staging system currently provides the best framework for predicting a patient’s prognosis. However, a major weakness of the TNM system is that significant survival differences exist even within its subgroups. The objective of this study was to create a simple tool to accurately predict patient survival from gastric cancer after gastrectomy. Methods: Between December 1986 to March 2007, 10,621 patients were surgically treated for gastric cancer at a single institution and observed until death. A nomogram was determined using Cox proportional hazard regression for multivariate analysis and the Kaplan-Meier method for estimation of 5-year overall survival. Overall survival was the endpoint. The predicted probability of the nomogram for actual overall survival was compared to the 7th edition AJCC TNM staging system. Then, the nomogram was validated using external data sets from four different institutions from Korea, Japan, and China. The number of patients in each data set was 1573 (A), 297 (B), 78 (C) and 767 patients (D). Results: Variables selected for the prediction model included age, gender, depth of invasion, number of metastatic lymph nodes (LN), total number of LN retrieved, and the presence of distant metastasis. The newly developed nomogram more accurately predicted a gastric cancer patient’s overall 5-year survival than the 7th Edition AJCC TNM system (p=0.0024) with area under the curve 0.8023 (our nomogram) and 0.7869 (AJCC TNM staging system). The concordance indexes of the different validation sets were 0.824 (A), 0.835 (B), 0.916 (C), and 0.767 (D). Conclusions: Our simple nomogram requires minimal patient and tumor information. It accurately predicts the 5-year overall survival for a patient with gastric cancer after surgical resection. Already internationally validated with data sets of various sample sizes and from different countries, our new nomogram provides a useful tool for prognostication after gastrectomy with wide applicability in different patient populations and institutions.

scholarly journals Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257949
Author(s):  
Charles Q. Yang ◽  
Huan Wang ◽  
Zhenqiu Liu ◽  
Matthew T. Hueman ◽  
Aadya Bhaskaran ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Staging System ◽  
Tnm Staging ◽  
P Value ◽  
Survival Prediction ◽  
Training Set ◽  
Prognostic System ◽  
Tnm Staging System ◽  
Prognostic Groups ◽  
Validation Set

Background Integrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. Methods and findings Cancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10−23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman’s rank correlation coefficient = 0.8316; p-value = 4.5×10−13). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8×10−43). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems. Conclusions The EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.

Prognostic nomogram to predict overall survival for patients with perihilar cholangiocarcinoma: A population-based study of SEER database (Preprint)

2020 ◽  
Author(s):  
Chendong Wang
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Staging System ◽  
Tnm Staging ◽  
Survival Outcome ◽  
Perihilar Cholangiocarcinoma ◽  
Cox Regression Analysis ◽  
Calibration Curves ◽  
Tnm Staging System ◽  
Net Benefits

BACKGROUND Perihilar cholangiocarcinoma (pCCA) is a highly aggressive malignancy with poor prognosis. Accurate prediction is of great significance for patients’ survival outcome. OBJECTIVE The present study aimed to propose a prognostic nomogram for predicting the overall survival (OS) for patients with pCCA. METHODS We conducted a retrospective analysis in a total of 940 patients enrolled from the Surveillance, Epidemiology, and End Results (SEER) program and developed a nomogram based on the prognostic factors identified from the cox regression analysis. Concordance index (C-index), risk group stratification and calibration curves were adopted to test the discrimination and calibration ability of the nomogram with bootstrap method. Decision curves were also plotted to evaluate net benefits in clinical use against TNM staging system. RESULTS On the basis of multivariate analysis, five independent prognostic factors including age, summary stage, surgery, chemotherapy, together with radiation were selected and entered into the nomogram model. The C-index of the model was significantly higher than TNM system in the training set (0.703 vs 0.572, P<0.001), which was also proved in the validation set (0.718 vs 0.588, P<0.001). The calibration curves for 1-, 2-, and 3-year OS probabilities exhibited good agreements between the nomogram-predicted and the actual observation. Decision curves displayed that the nomogram obtained more net benefits than TNM staging system in clinical context. The OS curves of two distinct risk groups stratified by nomogram-predicted survival outcome illustrated statistical difference. CONCLUSIONS We established and validated an easy-to-use prognostic nomogram, which can provide more accurate individualized prediction and assistance in decision making for pCCA patients.

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