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

Wang, Jinkui; Zhanghuang, Chenghao; Tan, Xiaojie; Mi, Tao; Liu, Jiayan; Jin, Liming; Li, Mujie; Zhang, Zhaoxia; He, Dalin

doi:10.3389/fpubh.2022.840525

Cited by 5 publications

(6 citation statements)

References 47 publications

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“…The cumulative risk model was used to estimate the cumulative incidence of cancer-specific death. Based on Fine and Gray's proportional sub-distribution hazard model, the influencing factors of cancer-specific death were analyzed [ 19 , 20 ]. Proportional sub-distribution hazard model is a direct extension of Cox model in competitive risk situations.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a nomogram to predict cancer-specific survival in elderly patients with papillary thyroid carcinoma: a population-based study

et al. 2022

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Objective Thyroid carcinoma (TC) is the most common endocrine tumor in the human body. Papillary thyroid carcinoma (PTC) accounts for more than 80% of thyroid cancers. Accurate prediction of elderly PTC can help reduce the mortality of patients. We aimed to construct a nomogram predicting cancer-specific survival (CSS) in elderly patients with PTC. Methods Patient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program. Univariate and multivariate Cox regression models were used to screen the independent risk factors for patients with PTC. The nomogram of elderly patients with PTC was constructed based on the multivariate Cox regression model. We used the concordance index (C-index), the area under the receiver operating characteristic curve (AUC) and the calibration curve to test the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to test the clinical value of the model. Results A total of 14,138 elderly patients with PTC were included in this study. Patients from 2004 to 2015 were randomly divided into a training set (N = 7379) and a validation set (N = 3141), and data from 2016 to 2018 were divided into an external validation set (N = 3618). Proportional sub-distribution hazard model showed that age, sex, tumor size, histological grade, TNM stage, surgery and chemotherapy were independent risk factors for prognosis. In the training set, validation set and external validation set, the C-index was 0.87(95%CI: 0.852–0.888), 0.891(95%CI: 0.866–0.916) and 0.931(95%CI:0.894–0.968), respectively, indicating that the nomogram had good discrimination. Calibration curves and AUC suggest that the prediction model has good discrimination and accuracy. Conclusions We constructed a new nomogram to predict CSS in elderly patients with PTC. Internal cross-validation and external validation indicate that the model has good discrimination and accuracy. The predictive model can help doctors and patients make clinical decisions.

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Section: Methodsmentioning

confidence: 99%

Development and validation of a nomogram to predict cancer-specific survival in elderly patients with papillary thyroid carcinoma: a population-based study

et al. 2022

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“…The accurate prediction of cancer control outcomes in RCC patients is important for counselling, the planning of follow-up and the selection of appropriate adjuvant trial designs. Several prognostic models incorporating clinical and pathological RCC variables have been validated to predict recurrence and mortality after nephrectomy in chRCC [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Of these, two models are recommended by European guidelines to predict cancer control outcomes after nephrectomy in non-metastatic chRCC [ 15 ]: Leibovich 2018 [ 2 , 16 ] and GRade, Age, Nodes and Tumor (GRANT) [ 3 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Critical Appraisal of Leibovich 2018 and GRANT Models for Prediction of Cancer-Specific Survival in Non-Metastatic Chromophobe Renal Cell Carcinoma

Piccinelli

Morra

Tappero

et al. 2023

Cancers

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Within the Surveillance, Epidemiology, and End Results database (2000–2019), we identified 5522 unilateral surgically treated non-metastatic chromophobe kidney cancer (chRCC) patients. This population was randomly divided into development vs. external validation cohorts. In the development cohort, the original Leibovich 2018 and GRANT categories were applied to predict 5- and 10-year cancer-specific survival (CSS). Subsequently, a novel multivariable nomogram was developed. Accuracy, calibration and decision curve analyses (DCA) tested the Cox regression-based nomogram as well as the Leibovich 2018 and GRANT risk categories in the external validation cohort. The accuracy of the Leibovich 2018 and GRANT models was 0.65 and 0.64 at ten years, respectively. The novel prognostic nomogram had an accuracy of 0.78 at ten years. All models exhibited good calibration. In DCA, Leibovich 2018 outperformed the novel nomogram within selected ranges of threshold probabilities at ten years. Conversely, the novel nomogram outperformed Leibovich 2018 for other values of threshold probabilities. In summary, Leibovich 2018 and GRANT risk categories exhibited borderline low accuracy in predicting CSS in North American non-metastatic chRCC patients. Conversely, the novel nomogram exhibited higher accuracy. However, in DCA, all examined models exhibited limitations within specific threshold probability intervals. In consequence, all three examined models provide individual predictions that might be suboptimal and be affected by limitations determined by the natural history of chRCC, where few deaths occur within ten years from surgery. Further investigations regarding established and novel predictors of CSS and relying on large sample sizes with longer follow-up are needed to better stratify CSS in chRCC.

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“…However, to date, there have been few studies exploring the influencing factors of death for localized PCa patients. In recent years, Cox model and competitive risk models have been gradually applied in the prediction of mortality for different cancers ( 11 , 12 ). Furthermore, it was reported that compared with the Cox model, the Fine-Gray proportional model for competing risks provides a better estimation for the risk of the main outcome of benefit when one or more competing risks exist ( 13 ).…”

Section: Introductionmentioning

confidence: 99%

Influencing factors for mortality in prostate cancer patients with T1 and T2 stage: a retrospective cohort study

Hong¹,

Cao²,

Chi³

et al. 2023

Transl Androl Urol

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Background: Few reports have focused on the influencing factors of localized prostate cancer (PCa)-specific mortality so far. This study aimed to develop a competitive risk model for identifying the factors influencing the localized PCa mortality rate based on 135,310 subjects in the Surveillance, Epidemiology, and End Results (SEER) database.Methods: We included 135,310 localized PCa male patients from SEER database 2004-2016 in this cohort study, and collected the baseline information of all patients, including age of diagnosis, race, marital status, socioeconomic status (SES), American Joint Committee on Cancer (AJCC) stage, prostate-specific antigen (PSA) Gleason score, and so on. The outcome was considered as PCa-specific mortality in this study. The end time of follow-up was November 2018. Independent risk factors were examined by multivariate Fine-Gray analysis. The results are shown by hazard ratio (HR) and 95% confidence interval (CI).Results: All patients were divided into three groups: died from localized PCa (n=1,400), died from other causes (n=16,996), and survived (n=116,914). The diagnostic age of 119,899 patients was ≥55 years. The multivariate Fine-Gray analysis indicated that age of diagnosis (55-70 years:

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

Cited by 5 publications

References 47 publications

Development and validation of a nomogram to predict cancer-specific survival in elderly patients with papillary thyroid carcinoma: a population-based study

Development and validation of a nomogram to predict cancer-specific survival in elderly patients with papillary thyroid carcinoma: a population-based study

Critical Appraisal of Leibovich 2018 and GRANT Models for Prediction of Cancer-Specific Survival in Non-Metastatic Chromophobe Renal Cell Carcinoma

Influencing factors for mortality in prostate cancer patients with T1 and T2 stage: a retrospective cohort study

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