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Wang X, Qian L, Qian Z, Wu Q, Cheng D, Wei J, Song L, Huang S, Chen X, Wang P, Weng G. Therapeutic options for different metastatic sites arising from renal cell carcinoma: A review. Medicine (Baltimore) 2024; 103:e38268. [PMID: 38788027 PMCID: PMC11124732 DOI: 10.1097/md.0000000000038268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Renal cell carcinoma (RCC) stands among the top 10 malignant neoplasms with the highest fatality rates. It exhibits pronounced heterogeneity and robust metastatic behavior. Patients with RCC may present with solitary or multiple metastatic lesions at various anatomical sites, and their prognoses are contingent upon the site of metastasis. When deliberating the optimal therapeutic approach for a patient, thorough evaluation of significant risk factors such as the feasibility of complete resection, the presence of oligometastases, and the patient's functional and physical condition is imperative. Recognizing the nuanced differences in RCC metastasis to distinct organs proves advantageous in contemplating potential treatment modalities aimed at optimizing survival outcomes. Moreover, discerning the metastatic site holds promise for enhancing risk stratification in individuals with metastatic RCC. This review summarizes the recent data pertaining to the current status of different RCC metastatic sites and elucidates their role in informing clinical management strategies across diverse metastatic locales of RCC.
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Affiliation(s)
- Xue Wang
- Zhejiang Key Laboratory of Pathophysiology, Ningbo University, Ningbo, China
| | - Lin Qian
- Department of Urologic Surgery, Ningbo Yinzhou No. 2 Hospital, Ningbo, China
| | - Zengxing Qian
- Zhejiang Key Laboratory of Pathophysiology, Ningbo University, Ningbo, China
| | - Qihang Wu
- Department of Clinical Laboratory, Ningbo Yinzhou No. 2 Hospital, Ningbo, China
| | - Dongying Cheng
- Department of community, Ningbo Yinzhou No. 3 Hospital, Ningbo, China
| | - Junjun Wei
- Zhejiang Key Laboratory of Pathophysiology, Ningbo University, Ningbo, China
| | - Lingmin Song
- Zhejiang Key Laboratory of Pathophysiology, Ningbo University, Ningbo, China
| | - Shuaihuai Huang
- Zhejiang Key Laboratory of Pathophysiology, Ningbo University, Ningbo, China
| | - Xiaodong Chen
- Zhejiang Key Laboratory of Pathophysiology, Ningbo University, Ningbo, China
| | - Ping Wang
- Department of Clinical Laboratory, Ningbo Yinzhou No. 2 Hospital, Ningbo, China
| | - Guobin Weng
- Zhejiang Key Laboratory of Pathophysiology, Ningbo University, Ningbo, China
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Xu C, Zhou Q, Liu W, Li W, Dong S, Li W, Xu X, Qiao X, Jiang Y, Chen J, Yin C. Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients. JOURNAL OF ONCOLOGY 2022; 2022:5798602. [PMID: 36276292 PMCID: PMC9586755 DOI: 10.1155/2022/5798602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/06/2022] [Accepted: 06/30/2022] [Indexed: 11/23/2022]
Abstract
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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Affiliation(s)
- Chan Xu
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Qian Zhou
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Chong Qing Liang Jiang New Area, Chongqing, China
| | - Wencai Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wenle Li
- Xiamen University, Molecular Imaging and Translational Medicine Research Center, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen, China
| | - Shengtao Dong
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian 116000, China
| | - Wanying Li
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Xiaofeng Xu
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
- Department of Urology, Xianyang Central Hospital, Xianyang, China
| | - Ximin Qiao
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
- Department of Urology, Xianyang Central Hospital, Xianyang, China
| | - Youli Jiang
- Hengyang Medical School, School of Nursing, University of South China, Hengyang, Hunan, China
| | - Jingfang Chen
- Hengyang Medical School, School of Nursing, University of South China, Hengyang, Hunan, China
- National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, Shenzhen, Guangdong, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
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Chen T, Zhan X, Du J, Liu X, Deng W, Zhao S, Jiang M, Xiong Y, Zhang X, Chen L, Fu B. A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study. Front Surg 2022; 9:871577. [PMID: 35392061 PMCID: PMC8980350 DOI: 10.3389/fsurg.2022.871577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 02/25/2022] [Indexed: 12/24/2022] Open
Abstract
Background Metastatic 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. Methods The 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). Results There 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. Conclusions The 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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Affiliation(s)
- Tao Chen
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiangpeng Zhan
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Junfu Du
- Department of Urology, Wuning People's Hospital, Jiujiang, China
| | - Xiaoqiang Liu
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wen Deng
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Shuaishuai Zhao
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ming Jiang
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yunqiang Xiong
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaohai Zhang
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Luyao Chen
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Jiangxi Institute of Urology, Nanchang, China
- Luyao Chen
| | - Bin Fu
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Jiangxi Institute of Urology, Nanchang, China
- *Correspondence: Bin Fu
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Wang J, Zhanghuang C, Tan X, Mi T, Liu J, Jin L, Li M, Zhang Z, He D. Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma. Front Public Health 2022; 9:831940. [PMID: 35155365 PMCID: PMC8831843 DOI: 10.3389/fpubh.2021.831940] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 12/24/2021] [Indexed: 12/09/2022] Open
Abstract
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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Affiliation(s)
- Jinkui Wang
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Chenghao Zhanghuang
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
- Department of Urology, Yunnan Key Laboratory of Children's Major Disease Research, Kunming Children's Hospital (Children's Hospital Affiliated to Kunming Medical University), Kunming, China
| | - Xiaojun Tan
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
- Department of Urology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical University, Nanchong, China
| | - Tao Mi
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jiayan Liu
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Liming Jin
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Mujie Li
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Zhaoxia Zhang
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Dawei He
- Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Dawei He
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