A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases

Sheng, Xinyu; Lu, Xuan; Wu, Jian; Chen, Lu; Cao, Hong

doi:10.1155/2021/6627562

Cited by 7 publications

(9 citation statements)

References 29 publications

(24 reference statements)

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“…Although the treatment of mRCC has made progress in the past two decades, the prognosis of patients with mRCC remains dismal ( 15 ). Previous studies on mRCC generally concentrated on the long-term survival of patients ( 16 , 17 ) or risk factors related to lung and bone metastasis ( 18 , 19 ). However, advanced or highly invasive tumors often lead to early death and few study has identified risk factors associated with early death in 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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“…9 variables with nonzero coefficients were selected by optimal lambda. 6 Journal of Oncology cell carcinoma [20]. ese risk factors were also important in other distant metastases of kidney cancer [1,7].…”

Section: Discussionmentioning

confidence: 99%

“…Although some studies have reported some biomarkers other than the above predictors for LM prediction [26], few of these markers have been applied. Previously, Xinyu Sheng's team at the Zhejiang University School predicted LM in kidney cancer patients based on patient data from the SEER database, with a column line plot of development and a model constructed based on TNM stages with ground AUC of 0.780 and 0.618, respectively, and the study was not externally validated [20,27]. Although the AUC of the models developed in the training set is greater than 0.50, there is still room for improvement.…”

Section: Discussionmentioning

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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“…The nomograms prediction model represented by UISS (9), SSIGN (10), Leibovich (11) was born and provided treatment suggestions for clinicians. In terms of the RCC transfer prediction model, we found that the prediction models for each leading transfer site of RCC were reported (12)(13)(14), but the C-index of the model was 0.714-0.803, and the area under the curve (AUC) was 0.767-0.780, indicating that the relative accuracy was insufficient. And the distant metastasis prediction model for the overall large sample of the elderly has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma

Wang

Zhanghuang

Tan

et al. 2022

Front. Public Health

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BackgroundRenal cell carcinoma (RCC) is the most common renal malignant tumor in elderly patients. The prognosis of renal cell carcinoma with distant metastasis is poor. We aim to construct a nomogram to predict the risk of distant metastasis in elderly patients with RCC to help doctors and patients with early intervention and improve the survival rate.MethodsThe clinicopathological information of patients was downloaded from SEER to identify all elderly patients with RCC over 65 years old from 2010 to 2018. Univariate and multivariate logistic regression analyzed the training cohort's independent risk factors for distant metastasis. A nomogram was established to predict the distant metastasis of elderly patients with RCC based on these risk factors. We used the consistency index (C-index), calibration curve, and area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical application value of the model.ResultsA total of 36,365 elderly patients with RCC were included in the study. They were randomly divided into the training cohort (N = 25,321) and the validation cohort (N = 11,044). In the training cohort, univariate and multivariate logistic regression analysis suggested that race, tumor histological type, histological grade, T stage, N stage, tumor size, surgery, radiotherapy, and chemotherapy were independent risk factors for distant metastasis elderly patients with RCC. A nomogram was constructed to predict the risk of distant metastasis in elderly patients with RCC. The training and validation cohort's C-indexes are 0.949 and 0.954, respectively, indicating that the nomogram has excellent accuracy. AUC of the training and validation cohorts indicated excellent predictive ability. DCA suggested that the nomogram had a better clinical application value than the traditional TN staging.ConclusionThis study constructed a new nomogram to predict the risk of distant metastasis in elderly patients with RCC. The nomogram has excellent accuracy and reliability, which can help doctors and patients actively monitor and follow up patients to prevent distant metastasis of tumors.

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A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases

Cited by 7 publications

References 29 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

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

Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma

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