Nomograms-based prediction of overall and cancer-specific survivals for patients with chromophobe renal cell carcinoma

Chen, Chunyang; Geng, Xinyu; Liang, Rui; Zhang, Dongze; Sun, Meiyun; Zhang, Guangbo; Hou, Jianquan

doi:10.1177/1535370220977107

Cited by 6 publications

(8 citation statements)

References 32 publications

(31 reference statements)

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“…Variables including pathological grade, T-stage, and N-stage associated with patient overall survival were also shown to be related to BMs ( 19 , 24 – 26 ). A possible explication for the higher risk of BMs in mRCC patients with high-level pathological grade and advanced T-stage and N-stage could be the possibility to possess drastic aggressiveness.…”

Section: Discussionmentioning

confidence: 96%

“…Women more frequently use healthcare system, including the scheduled abdominal radiological examination, resulting in early detection, which may further contribute to the difference of BMs in RCC (24). Variables including pathological grade, T-stage, and N-stage associated with patient overall survival were also shown to be related to BMs (19,(24)(25)(26). A possible explication for the higher risk of BMs in mRCC patients with high-level pathological grade and advanced T-stage and N-stage could be the possibility to possess drastic aggressiveness.…”

Section: Discussionmentioning

confidence: 99%

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Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

et al. 2021

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BackgroundBone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.MethodsPatients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.ResultsA total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.ConclusionsThe comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

et al. 2021

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“…Another advantage of our study is that it constructed nomograms of OS and CSS of patients with chRCC after nephrectomy by population-based data from the SEER database with sufficiently large samples and an external validation cohort, which can increase the validity and effectiveness of our conclusions. Although one study conducted by Chen et al 11 also constructed nomograms of OS and CSS for patients with chRCC, our study has some differences. First, the collected data in our study are for patients after nephrectomy, and the study carried out by Chen et al 11 is for all patients with chRCC.…”

Section: Discussionmentioning

confidence: 91%

“…Currently, various nomograms have been designed and used in the prognosis evaluation for patients with RCC, but most are for ccRCC and pRCC. Although Chen, et al 11 constructed a nomogram for chRCC patients, it is for all chRCC patients and it is not verified by an external validation cohort, there is still not a nomogram for chRCC patients with surgery treatment. Therefore, the purpose of our study was to construct nomograms based on data from the Surveillance, Epidemiology and End Results (SEER) database to predict overall survival (OS) and cancer-specific survival (CSS) for chRCC patients undergoing surgery and to validate the nomogram with an internal SEER cohort and an individual cohort from our department.…”

Section: Introductionmentioning

confidence: 99%

Nomograms for predicting overall and cancer-specific survival of patients with chromophobe renal cell carcinoma after nephrectomy: a retrospective SEER-based study

Zheng

Zhao

et al. 2022

BMJ Open

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ObjectiveWe aimed to construct and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with chromophobe renal cell carcinoma (chRCC) after nephrectomy.DesignThis study is a retrospective cohort study.Setting and participantsThere were 2810 patients with chRCC from Surveillance, Epidemiology and End Results database diagnosed between 2010 and 2015 included in the study who were randomly divided into a training cohort (n=1970) and a validation cohort (n=840). Another single-centre external validation cohort containing 124 patients from our hospital was also involved in our study.Primary and secondary outcome measuresOS and CSS.ResultsNomograms for OS and CSS include four and five variables, respectively, from the result of least absolute shrinkage and selection operator regression analyses. Nomograms reveal the accurate discrimination by the area under the curve of receiver operating characteristic (ROC) curves and C-indexes, with a C-index value of 0.777 (95% CI 0.728 to 0.826), 0.810 (95% CI 0.747 to 0.873) and 0.863 (95% CI 0.773 to 0.953) for the training cohort, the internal validation cohort and the external validation cohort in the nomogram for OS; and a C-index value of 0.884 (95% CI 0.829 to 0.939), 0.868 (95% CI 0.772 to 0.964) and 0.862 (95% CI 0.760 to 0.964) for the training cohort, the internal validation cohort and the external validation cohort in the nomogram for CSS. It was also proven that there was a high degree of conformance between the predicted and observation results by calibration plots. In addition, the comparison of ROC curves and C-indexes between nomograms and seventh tumour, node and metastasis stage demonstrated that nomograms were better in accuracy and efficacy ability.ConclusionsWe successfully constructed two accurate and effective nomograms to predict OS and CSS for patients with chRCC after nephrectomy, which can help clinical doctors choose individual treatment strategies for chRCC patients.

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“…Nomogram is a data-based graphical computing tool that can estimate the risk of a disease based on staging systems such as the American Joint Commission on Cancer (AJCC) and other key risk factors related to prognosis (14). Today, many nomograms have been developed and applied to RCC, but mainly for patients with ccRCC and pRCC (15)(16)(17)(18). However, there is still a lack of adequate and reliable nomograms to predict the prognosis of elderly patients with chRCC.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Competitive Risk Model in Elderly Patients With Chromophobe Cell Renal Carcinoma: A Population-Based Study

Wang

Zhanghuang

Tan

et al. 2022

Front. Public Health

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BackgroundRenal cell carcinoma (RCC) is the most common renal malignancy in adults, and chromophobe renal cell carcinoma (chRCC) is the third most common subtype of RCC. We aimed to construct a competitive risk model to predict cancer-specific survival (CSS) in elderly patients with chRCC.MethodsThe clinicopathological information of the patients was downloaded from the SEER database, and the patients were randomly divided into the training and validation cohorts. Patients' risk factors for cancer-specific death (CSM) were analyzed using proportional subdistribution hazard (SH). We constructed a competitive risk model to predict the CSS of elderly chRCC patients. Consistency index (C-index), the area under receiver operating curve (AUC), and a calibration curve were used to validate the model's accuracy. Decision curve analysis (DCA) was used to test the clinical value of the model.ResultsA total of 3,522 elderly patients with chRCC were included in the analysis. Patients were randomly assigned to either the training cohort (N = 2,474) or the validation cohort (N = 1,048). SH analysis found that age, race, T, N, and M stage, tumor size, and surgery were risk factors for CSM. We constructed a competitive risk model to predict patients' CSS. In the training set, the model predicted patients' 1-, 3-, and 5-year CSS with C-indices of 82.2, 80.8, and 78.2, respectively. The model predicted patient 1-, 3-, and 5-year CSS in the validation cohort with C-indices of 84.7, 83.4, and 76.9, respectively. The calibration curve showed that the model's predicted value is almost consistent with the observed value, which indicated that the model has good accuracy. The AUC of the training set and validation queue also suggested that the model has good discrimination. The clinical utility of the DCA model in predicting patients' CSS is higher than that of traditional TNM staging.ConclusionsWe constructed a competitive risk model to predict CSS in elderly patients with chRCC. The model has good accuracy and reliability, which can help doctors and patients to make clinical decisions and follow-up strategies.

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Nomograms-based prediction of overall and cancer-specific survivals for patients with chromophobe renal cell carcinoma

Cited by 6 publications

References 32 publications

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Nomograms for predicting overall and cancer-specific survival of patients with chromophobe renal cell carcinoma after nephrectomy: a retrospective SEER-based study

Development and Validation of a Competitive Risk Model in Elderly Patients With Chromophobe Cell Renal Carcinoma: A Population-Based Study

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