Prognostic Factors and a Nomogram Predicting Overall Survival and Cancer-Specific Survival for Patients with Collecting Duct Renal Cell Carcinoma

Xiao, Ruotao; Liu, Cheng; He, Wei; Ma, Lulin

doi:10.1155/2021/6736008

Cited by 8 publications

(9 citation statements)

References 30 publications

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“…For patients in good condition, surgery can significantly reduce tumor burden and prolong overall survival ( 29 ). Collecting duct renal cell carcinoma (cdRCC) and sarcomatoid renal cell carcinoma (sRCC) were associated with an aggressive biology and characterized by a poor prognosis ( 30 , 31 ). Consistent with previous studies, histological classification such as cdRCC and sRCC was significantly related to early death of mRCC.…”

Section: Discussionmentioning

confidence: 99%

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Chen

Zhan

Du³

et al. 2022

Front. Surg.

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BackgroundMetastatic renal cell carcinoma (mRCC) is usually considered to have a poor prognosis, which has a high risk of early death (≤3 months). Our aim was to developed a predictive nomogram for early death of mRCC.MethodsThe SEER database was accessed to obtain the related information of 6,005 mRCC patients between 2010 and 2015. They were randomly divided into primary cohort and validation cohort in radio of 7:3. The optimal cut-off point regarding age at diagnosis and tumor size were identified by the X-tile analysis. Univariate and multivariate logistic regression models were applied to determine significant independent risk factors contributed to early death. A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA).ResultsThere were 6,005 patients with mRCC included in the predictive model, where 1,816 patients went through early death (death within ≤3 months of diagnosis), and among them 1,687 patients died of mRCC. Based on 11 significant risk factors, including age, grade, N-stage, histologic type, metastatic sites (bone, lung, liver and brain) and treatments (surgery, radiation, and chemotherapy), a practical nomogram was developed. The model's excellent effectiveness, discrimination and clinical practicality were proved by the AUC value, calibration plots and DCA, respectively.ConclusionsThe nomogram may play a major part in distinguishing the early death of mRCC patients, which can assist clinicians in individualized medicine.

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Section: Discussionmentioning

confidence: 99%

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Chen

Zhan

Du³

et al. 2022

Front. Surg.

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“…Recently, large cohort studies revealed that for CDC patients, CSS was similar to OS. This indicates that a vast majority of CDC patients eventually die due to CDC, further reflecting poor prognosis of CDC 4 , 7 . In the current study, the median OS and CSS time for CDC patients were 5 and 6 months, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Surgery is often chosen for management of CDC based on established standard treatments for common renal tumors. Previous studies demonstrated that patients receiving surgery survive longer than those who do not undergo surgery for CDC 4 , 7 . However, metastatic features of the tumor may prevent this potential survival benefit obtained from surgical treatment.…”

Section: Introductionmentioning

confidence: 99%

Impact of treatment modalities on prognosis of patients with metastatic renal collecting duct carcinoma

Qian

Wan

Tan

et al. 2022

Sci Rep

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Although patients with renal collecting duct carcinoma (CDC) benefit from surgery, the value of cytoreductive nephrectomy (CNx) for the prognosis of patients with metastatic CDC remains unclear. Hence, in this study, we used data from Surveillance, Epidemiology, and End Results (SEER) registry to investigate the prognostic factors and the impact of CNx on the outcomes in patients with metastatic CDC. Data of 521 patients, diagnosed with CDC between 2000 and 2018, were retrieved from the SEER database. Kaplan–Meier method and log-rank tests were used to compare the survival differences between the CNx group and non-surgical group. Multivariate Cox regression analysis was used to identify the risk factors associated with overall survival (OS) and cancer-specific survival (CSS) for patients with metastatic CDC. Moreover, multivariate Cox regression analysis guided by directed acyclic graphs (DAG) was used to unfold the impact of CNx and chemotherapy on OS and CSS. 86 patients were identified to have metastatic CDC. The median OS and CSS time were 5 and 6 months, respectively. The OS rates at 1-, 2- and 5-years were 24.4%, 15.1% and 2.3%, respectively. Whereas, the CSS rates at 1-, 2- and 5-years were 27.0%, 17.9% and 2.8%, respectively. Old patients and those receiving CNx or chemotherapy exhibited better survival outcomes. The multivariate regression model identified non-surgical treatment as the only independent prognostic factor for both, OS and CSS. However, DAG-guided multivariate Cox regression model showed that both, CNx and chemotherapy, were associated with both, OS and CSS. Patients with metastatic CDC exhibited worse clinical outcomes. However, CNx improved the prognosis of patients with metastatic CDC. Additionally, surgical resection of visible lesions and suitable chemotherapy were identified as alternative treatment strategies.

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“…As the result, when patients intend to seek a healing care, they may have been in a metastatic state of kidney cancer and are suffering from the corresponding complications. e fiveyear survival rates of stage I and II were about 88% to 95%, and cancer-specific survival (CSS) rates were 84% to 95% [4]. Renal cell carcinoma (RCC), making up 90% of kidney cancer, is the sixth and eighth most common cancer among American men and women in 2021 [5].…”

Section: Introductionmentioning

confidence: 99%

“…At the time of initial diagnosis, 18%-40% of patients have already developed systemic metastases. In addition, metastasis is widespread in the long-termfollow-up after nephrectomy [4,7,9,13,14]. e study of Jianxin Xue and colleagues reported that 2931 of 33449 RCC had distant metastasis and lung (6.19%) was the most common site of metastasis [7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients

Qian

Liu

et al. 2022

Journal of Oncology

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Objective. To establish and verify the clinical prediction model of lung metastasis in renal cancer patients. Method. Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model’s performance. The final model was shown by a website calculator. Result. Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879–0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model. Conclusion. This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation.

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Prognostic Factors and a Nomogram Predicting Overall Survival and Cancer-Specific Survival for Patients with Collecting Duct Renal Cell Carcinoma

Cited by 8 publications

References 30 publications

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Impact of treatment modalities on prognosis of patients with metastatic renal collecting duct carcinoma

Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients

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