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Nomogram Predicting Survival to Assist Decision-Making of Metastasectomy in Patients With Metastatic Renal Cell Carcinoma

ABSTRACT: The survival benefit of metastasectomy (MSX) in patients with metastatic renal cell carcinoma (mRCC) remains unclear. A reliable model to predict an individuals’ risk of cancer-specific mortality (CSM) and to identify optimal candidates for MSX is needed. We identified 2,911 mRCC patients who underwent cytoreductive nephrectomy from the Surveillance, Epidemiology, and End Results database (2010–2015). Based on the Fine and Gray competing risks analyses, we created a nomogram to predict the survival of mRCC patients. Decision tree analysis was useful for patient stratification. The impact of MSX was assessed among three different subgroups. Overall, 579 (19.9%) cases underwent MSX. In the entire patients, the 1-, 2-, and 3-year cumulative incidence of CSM were 32.8, 47.2, and 57.9%, respectively. MSX was significantly associated with improved survival (hazard ratio [HR] = 0.875, 95% confidence interval [CI] 0.773–0.991; P = 0.015). Based on risk scores, patients were divided into three risk groups using decision tree analysis. In the low-risk group, MSX was significantly associated with a 12.8% risk reduction of 3-year CSM (HR = 0.689, 95% CI 0.507–0.938; P = 0.008), while MSX was not associated with survival in intermediate- and high-risk groups. We proposed a novel nomogram and patient stratification approach to identify suitable patients for MSX. The newly identified patient subgroup with a low-risk of CSM might benefit more from aggressive surgery. These results should be further validated and improved by the prospective trials.

SUBMITTER: Wu K

PROVIDER: S-EPMC7793951 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:ObjectivesMen diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models.DesignSystematic literature review, pre-specified and registered on PROSPERO (CRD42018086394).Data sourcesMEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms.Eligibility criteriaInclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis.Data extraction and synthesisTitle, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool.ResultsDatabase searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available.ConclusionFew long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.

Project description:BackgroundOral tongue squamous cell carcinoma (OTSCC) is a devastating tumor with poor prognosis. There is an urgent need for reliable biomarkers to help predict prognosis and guide treatment for OTSCC. In the current study, we aimed to develop a robust multi-gene signature and prognostic nomogram to predict the prognosis of patients with non-distant metastatic OTSCC.MethodsOTSCC-related differentially-expressed genes were screened from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression based on 1,000 bootstrap replicates, LASSO regression and stepwise multivariate Cox regression were utilized to develop a novel multi-mRNA signature for predicting overall survival in OTSCC. The concordance index, area under receiver operating characteristic (ROC AUC) and calibration curve were employed to assess the prediction capacity of the novel multi-gene model. In addition, a prognostic nomogram was constructed to facilitate the clinical use of the fitted model. The Kaplan-Meier with log-rank test was employed to assess differences in overall survival.ResultsWe successfully established a novel 15-mRNA prognostic model for predicting overall survival of non-distant metastatic OTSCC, involving ADTRP, ITGA3, RFC4, CCDC96, CYP2J2, NELL2, SPHK1, SPAG16, HBEGF, S100A9, EGFL6, ADGRG6, PDE4D, ABCA4, and CTTN. The prediction ability of this 15-gene signature was independent of other clinicopathological factors, with an HR of 11.5 (95% CI: 4.70-28.3). Moreover, internal validation by bootstrap analysis yielded a C-index of 0.849, with a 3-year AUC of 0.907 and 5-year AUC of 0.944, which implied excellent prediction accuracy of the fitted model. In addition, external validation by using the GEO dataset (GSE41116) yielded a C-index of 0.804, with a 3-year AUC of 0.868 and 5-year AUC of 0.855, which also indicated good prediction ability of the 15-gene model. Finally, a prognostic nomogram integrating risk group, grade, T stage and N stage was established.ConclusionOur results demonstrate our 15-gene signature was independently associated with overall survival in non-distant metastatic OTSCC. Moreover, the prognostic nomogram integrating the 15-gene signature and clinicopathological factors has potential to be developed as a prognostic tool.

Project description:BackgroundThe optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial.MethodsWe selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model.ResultsA total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively.ConclusionsOur nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases.

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Nomogram Predicting Survival to Assist Decision-Making of Metastasectomy in Patients With Metastatic Renal Cell Carcinoma

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