Anita Robert, Ranjeeta Mallick, Daniel I McIsaac, Luke T Lavallée, Bimal Bhindi, Daniel Heng, Lori A Wood, Ricardo Rendon, Simon Tanguay, Anthony Finelli, Rahul K Bansal, Aly-Khan Lalani, Naveen Basappa, Miles P Mannas, Jasmir G Nayak, Georg A Bjarnason, Jean-Baptiste Lattouf, Frédéric Pouliot, Patrick O Richard, Camilla Tajzler, Rodney H Breau
{"title":"肾细胞癌复发、癌症特异性死亡率和全因死亡率预后模型的验证。","authors":"Anita Robert, Ranjeeta Mallick, Daniel I McIsaac, Luke T Lavallée, Bimal Bhindi, Daniel Heng, Lori A Wood, Ricardo Rendon, Simon Tanguay, Anthony Finelli, Rahul K Bansal, Aly-Khan Lalani, Naveen Basappa, Miles P Mannas, Jasmir G Nayak, Georg A Bjarnason, Jean-Baptiste Lattouf, Frédéric Pouliot, Patrick O Richard, Camilla Tajzler, Rodney H Breau","doi":"10.1097/JU.0000000000004348","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Postoperative prognostic tools allow for improved prediction of future recurrence risk, patient counseling, assessment of eligibility for adjuvant treatments, and appropriate follow-up surveillance. The purpose of this analysis was to validate prognostic models for patients with kidney cancer.</p><p><strong>Materials and methods: </strong>The Canadian Kidney Cancer information system is a prospective cohort of patients managed at 14 institutions since January 1, 2011, to present. The Canadian Kidney Cancer information system was used to assess 15 predictive models for kidney cancer recurrence, 6 for cancer-specific mortality, and 4 for all-cause mortality in patients with a solitary, nonmetastatic kidney tumor treated with surgery (partial or radical nephrectomy). Discrimination was measured using C statistics, 5-year calibration plots for calibration, and decision curve analysis at 5 years after surgery for net benefit when considering adjuvant therapy.</p><p><strong>Results: </strong>Seven thousand one hundred seventy-four patients were included. For kidney cancer recurrence, C statistics ranged from 0.62 to 0.83, depending on whether the model was derived and applied to all patients without further stratification, specific risk groups, or specific histologic subtypes. Cancer-specific mortality models had C statistics ranging from 0.60 to 0.89 and all-cause mortality models from 0.60 to 0.73. Using decision curve analysis in patients with clear-cell renal cell carcinoma, the best models for choosing adjuvant therapy to prevent recurrence and cancer-related death were the Mayo Clinic prediction models.</p><p><strong>Conclusions: </strong>Model performance varied considerably with some suitable for clinical use. If using prediction models to select adjuvant therapy, the Mayo Clinic models were best when applied to a large contemporary cohort of Canadian patients.</p>","PeriodicalId":17471,"journal":{"name":"Journal of Urology","volume":" ","pages":"455-466"},"PeriodicalIF":5.9000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Validation of Prognostic Models for Renal Cell Carcinoma Recurrence, Cancer-Specific Mortality, and All-Cause Mortality.\",\"authors\":\"Anita Robert, Ranjeeta Mallick, Daniel I McIsaac, Luke T Lavallée, Bimal Bhindi, Daniel Heng, Lori A Wood, Ricardo Rendon, Simon Tanguay, Anthony Finelli, Rahul K Bansal, Aly-Khan Lalani, Naveen Basappa, Miles P Mannas, Jasmir G Nayak, Georg A Bjarnason, Jean-Baptiste Lattouf, Frédéric Pouliot, Patrick O Richard, Camilla Tajzler, Rodney H Breau\",\"doi\":\"10.1097/JU.0000000000004348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Postoperative prognostic tools allow for improved prediction of future recurrence risk, patient counseling, assessment of eligibility for adjuvant treatments, and appropriate follow-up surveillance. The purpose of this analysis was to validate prognostic models for patients with kidney cancer.</p><p><strong>Materials and methods: </strong>The Canadian Kidney Cancer information system is a prospective cohort of patients managed at 14 institutions since January 1, 2011, to present. The Canadian Kidney Cancer information system was used to assess 15 predictive models for kidney cancer recurrence, 6 for cancer-specific mortality, and 4 for all-cause mortality in patients with a solitary, nonmetastatic kidney tumor treated with surgery (partial or radical nephrectomy). Discrimination was measured using C statistics, 5-year calibration plots for calibration, and decision curve analysis at 5 years after surgery for net benefit when considering adjuvant therapy.</p><p><strong>Results: </strong>Seven thousand one hundred seventy-four patients were included. For kidney cancer recurrence, C statistics ranged from 0.62 to 0.83, depending on whether the model was derived and applied to all patients without further stratification, specific risk groups, or specific histologic subtypes. Cancer-specific mortality models had C statistics ranging from 0.60 to 0.89 and all-cause mortality models from 0.60 to 0.73. Using decision curve analysis in patients with clear-cell renal cell carcinoma, the best models for choosing adjuvant therapy to prevent recurrence and cancer-related death were the Mayo Clinic prediction models.</p><p><strong>Conclusions: </strong>Model performance varied considerably with some suitable for clinical use. If using prediction models to select adjuvant therapy, the Mayo Clinic models were best when applied to a large contemporary cohort of Canadian patients.</p>\",\"PeriodicalId\":17471,\"journal\":{\"name\":\"Journal of Urology\",\"volume\":\" \",\"pages\":\"455-466\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Urology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/JU.0000000000004348\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/2 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Urology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/JU.0000000000004348","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/2 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
Validation of Prognostic Models for Renal Cell Carcinoma Recurrence, Cancer-Specific Mortality, and All-Cause Mortality.
Purpose: Postoperative prognostic tools allow for improved prediction of future recurrence risk, patient counseling, assessment of eligibility for adjuvant treatments, and appropriate follow-up surveillance. The purpose of this analysis was to validate prognostic models for patients with kidney cancer.
Materials and methods: The Canadian Kidney Cancer information system is a prospective cohort of patients managed at 14 institutions since January 1, 2011, to present. The Canadian Kidney Cancer information system was used to assess 15 predictive models for kidney cancer recurrence, 6 for cancer-specific mortality, and 4 for all-cause mortality in patients with a solitary, nonmetastatic kidney tumor treated with surgery (partial or radical nephrectomy). Discrimination was measured using C statistics, 5-year calibration plots for calibration, and decision curve analysis at 5 years after surgery for net benefit when considering adjuvant therapy.
Results: Seven thousand one hundred seventy-four patients were included. For kidney cancer recurrence, C statistics ranged from 0.62 to 0.83, depending on whether the model was derived and applied to all patients without further stratification, specific risk groups, or specific histologic subtypes. Cancer-specific mortality models had C statistics ranging from 0.60 to 0.89 and all-cause mortality models from 0.60 to 0.73. Using decision curve analysis in patients with clear-cell renal cell carcinoma, the best models for choosing adjuvant therapy to prevent recurrence and cancer-related death were the Mayo Clinic prediction models.
Conclusions: Model performance varied considerably with some suitable for clinical use. If using prediction models to select adjuvant therapy, the Mayo Clinic models were best when applied to a large contemporary cohort of Canadian patients.
期刊介绍:
The Official Journal of the American Urological Association (AUA), and the most widely read and highly cited journal in the field, The Journal of Urology® brings solid coverage of the clinically relevant content needed to stay at the forefront of the dynamic field of urology. This premier journal presents investigative studies on critical areas of research and practice, survey articles providing short condensations of the best and most important urology literature worldwide, and practice-oriented reports on significant clinical observations.