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 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.

Proposal for Reclassification of the TNM Staging System in Patients with Locally Advanced (pT3–4) Renal Cell Carcinoma According to the Cancer-Related Outcome

2007 ◽  
Vol 51 (3) ◽  
pp. 722-731 ◽  
Author(s):  
Vincenzo Ficarra ◽  
Giacomo Novara ◽  
Massimo Iafrate ◽  
Livio Cappellaro ◽  
Emiliano Bratti ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Locally Advanced ◽  
Staging System ◽  
Tnm Staging ◽  
Tnm Staging System

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.

Pathological complete response after neoadjuvant/induction treatment: where is its place in the lung cancer staging system?†

2019 ◽  
Vol 56 (3) ◽  
pp. 604-611 ◽  
Author(s):  
Hüseyin Melek ◽  
Gamze Çetinkaya ◽  
Erhan Özer ◽  
Eylem Yentürk ◽  
Tolga Evrim Sevinç ◽  
...  
Keyword(s):  
Locally Advanced ◽  
Staging System ◽  
Complete Response ◽  
Tnm Staging ◽  
Induction Treatment ◽  
Term Survival ◽  
Tnm Staging System ◽  
Locally Advanced Nsclc ◽  
Group 2 ◽  
Group 1

Abstract OBJECTIVES Prognosis for patients with non-small-cell lung cancer (NSCLC) who, after neoadjuvant/induction and surgery, have a pathological complete response (pCR) is expected to be improved. However, the place of the pCR patients in the context of the tumour, lymph node and metastasis (TNM) staging system is still not defined. The aim of this study is to investigate the long-term survival of NSCLC patients with pCR and to find their appropriate staging category within the TNM staging system. METHODS We retrospectively reviewed the prospectively recorded data of 1076 patients undergoing surgery (segmentectomy or more) for NSCLC between 1996 and 2016. Patients were divided into 2 groups. Group 1: clinical early-stage patients who underwent direct surgical resection (n = 660); group 2: patients who received neoadjuvant/induction treatment before surgical resection for locally advanced NSCLC (n = 416). Morbidity, mortality, survival rates and prognostic factors were analysed and compared. RESULTS Postoperative histopathological evaluation revealed pCR in 72 (17%) patients in group 2. Overall 5-year survival was 58.7% (group 1 = 62.3%, group 2 = 52.8%, P = 0.001). Of note, 5-year survival was 72.2% for pCRs. In addition, 5-year survival for stage 1a disease was 82.6% in group 1 and 63.2% in group 2 (P = 0.008); 70.3% in group 1 and 60.5% in group 2 for stage 1b (P = 0.08). Patients with stage II had a 5-year survival of 53.9% in group 1 and 51.1% in group 2 (P = 0.36). CONCLUSIONS This study shows that patients with locally advanced NSCLC developing a pCR after neoadjuvant/induction treatment have the best long-term survival and survival similar that of to stage Ib patients.

Multiple immune features based signature for predicting recurrence and survival of inoperable la-NSCLC patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21066-e21066
Author(s):  
Meiying Guo ◽  
Wanlong Li ◽  
Bingjie Fan ◽  
Bing Zou ◽  
Xindong Sun ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Immune Status ◽  
Cox Regression ◽  
Locally Advanced ◽  
Staging System ◽  
Tnm Staging ◽  
Immune Signature ◽  
Complementary Role ◽  
Tnm Staging System ◽  
Nsclc Patients

e21066 Background: The immune status of tumor microenvironment is extremely complex. One single immune feature cannot reflect the integral immune status and its prognostic value was limited. We postulated that the immune signature based on multiple immuno-features could markedly improve the prediction of post-chemoradiotherapeutic survival in inoperable locally advanced non-small-cell lung cancer (LA-NSCLC) patients. Methods: In this study, 100 patients who were diagnosed as inoperable LA-NSCLC between January 2005 and January 2016 were analyzed. A 5-immune feature-based signature was then constructed using the nested repeat 10-fold cross validation with LASSO Cox regression model. Nomograms were then established for predicting prognosis. Results: Immune signature combining 5 immuno-features were significantly associated with OS and PFS (P = 0.002 and P = 0.014, respectively) in patients with inoperable LA-NSCLC, and at a cutoff of -0.198 stratified patients into two groups with 5-year OS rates of 39.8% and 8.8%, and 2-year PFS rates of 22.2% and 5.5% for the high- and low-immune signature groups, respectively. Using immune signature, we proposed immune signature nomograms, which were better than the traditional TNM staging system in terms of discriminating ability (OS: 0.692 vs. 0.588; PFS: 0.672 vs. 0.586, respectively) or net weight classification (OS: 32.96%; PFS: 9.22%), suggesting that immune signature plays a complementary role in the prognosis prediction of patients with inoperable LA-NSCLC. Conclusions: Multiple immune features based immune signature could effectively predict recurrence and survival of inoperable LA-NSCLC patients, and complemented the prognostic value of the TNM staging system.

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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