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Nomogram to predict risk and prognosis of synchronous lung metastasis in renal cell carcinoma: A large cohort analysis.

ABSTRACT: We aimed to construct and validate nomogram models that predict the incidence of lung metastasis (LM) in patients with renal cell carcinoma (RCC) and evaluate overall survival (OS) and cancer-specific survival (CSS) among RCC patients with LM. The Surveillance, Epidemiology, and End Results database was analyzed for RCC patients diagnosed between 2010 and 2015. The X-tile program was used to determine the best cutoff values for age at initial diagnosis and tumor size. Logistic regression analysis was performed to explore independent risk factors for LM, and COX regression analysis was used to identify prognostic indicators for OS and CSS in lung metastatic RCC patients. Subsequently, 3 nomograms were established, and receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were utilized to validate their accuracy. We randomly assigned 10,929 patients with RCC to 2 groups with 1:1 allocation. Multivariate logistic analyses revealed that pathology, tumor (T) stage, nodes (N) stage, race, grade, surgery, metastatic sites, and tumor size were independent risk factors for LM. Multivariate Cox analyses showed that pathology, T stage, N stage, age, surgery, metastatic sites, and residence were independent prognostic factors for OS and CSS in patients with LM. Then, nomograms were developed based on the multivariate logistic and Cox regression analyses results. The ROC and DCA curves confirmed that these nomograms achieved satisfactory discriminative power. Three effective nomograms were constructed and validated that can be used to assist clinicians in predicting the incidence of LM and evaluating the prognosis of lung metastatic RCC.

SUBMITTER: Lu Z

PROVIDER: S-EPMC9259154 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

Project description:Background and aimHepatocellular carcinoma is a common malignant tumor of the digestive system with a poor prognosis. The high recurrence rate and metastasis after surgery reduce the survival time of patients. Therefore, assessing the overall survival of patients with hepatocellular carcinoma after hepatectomy is critical to clinicians' clinical decision-making. Conventional hepatocellular carcinoma assessment systems (such as tumor lymph node metastasis and Barcelona clinical hepatocellular carcinoma) are obviously insufficient in assessing the overall survival rate of patients. This research is devoted to the development of nomogram assessment tools to assess the overall survival probability of patients undergoing liver resection.MethodsWe collected the clinical and pathological information of 438 hepatocellular carcinoma patients undergoing surgery from The Cancer Genome Atlas (TCGA) database, then excluded 87 patients who did not meet inclusion criteria. Univariate and multivariate analyses were performed on patient characteristics and related pathological factors. Finally, we developed a nomogram model to predict patient's prognosis.ResultsA retrospective analysis of 438 consecutive samples from the TCGA database of patients with hepatocellular carcinoma who underwent potentially curative liver resections. Six risk factors were included in the final model. In the training set, the discriminative ability of the nomogram was very good (concordance index = 0.944), and the external verification method (concordance index = 0.962) was used for verification. At the same time, the internal and external calibration of the model was verified, showing that the model was well calibrated. The calibration between the evaluation of the nomogram and the actual observations was good. According to the patient's risk factors, we determined the patient's Kaplan-Meyer survival analysis curve. Finally, the clinical decision curve was used to compare the benefits of two different models in evaluating patients' clinical outcomes.ConclusionsThe nomogram can be used to evaluate the post-hepatectomy 1-, 3-, and 5-year survival rates of patients with hepatocellular carcinoma. The Kaplan-Meyer curve can intuitively display the survival differences among patients with various risk factors. The clinical decision curve is a good reference guide for clinical application.

Project description:Background: Radiotherapy is a primary treatment strategy for patients with unresectable hepatocellular carcinoma (HCC); however, the prognostic factors among HCC patients who have received radiotherapy but not undergone surgery have not been systematically studied. Thus, the prognostic factors were investigated in this study based on the Surveillance, Epidemiology, and End Results (SEER) Medicare database. Methods: A screening process was used for select cases from the SEER database. Survival was analyzed using the Kaplan-Meier method and log-rank test, the Cox proportional hazards regression model, and a competing risk model. A nomogram was established for predicting 1- and 3-year overall survival (OS) of patients. Results: A total of 1305 HCC patients who received radiotherapy but had not undergone surgery were included in this study and divided into training (n = 1175) and validation cohorts (n = 130). Patients in the training cohort had a 1-year OS rate of 30.9±1.3%, a 3-year OS rate of 10.0±1.0%, and a median survival rate of 6.0 months (range, 5.4-6.6 months). Race (p = 0.025), T stage (p < 0.001), N stage (p < 0.001), M stage (p < 0.001), and chemotherapy (p < 0.001) were identified as independent risk factors by multivariate analyses in the training cohort, while sex, age, grade, marital status, and insurance status were not independent factors. Survival in patients who received radiotherapy was worse with respect to the following characteristics: black race; higher T, N, or M stage; and no chemotherapy. A nomogram was established based on the results of the multivariate analysis, which was internally validated by a concordance index (C-index) of 0.731±0.016 and a group of calibration plots. External validation was carried out and the C-index was 0.738±0.049, which demonstrated the effectiveness of the nomogram we constructed. Conclusions: Race, T stage, N stage, M stage, and chemotherapy were independent risk factors for survival of HCC patients who received radiotherapy but had not undergone surgery. A validated nomogram was formulated to predict 1- and 3-year OS in these patients based on individual clinical characteristics.

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Nomogram to predict risk and prognosis of synchronous lung metastasis in renal cell carcinoma: A large cohort analysis.

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