Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma.
ASSURE trial
Disease-free survival
Prognostic model
Renal cell carcinoma
Journal
European urology
ISSN: 1873-7560
Titre abrégé: Eur Urol
Pays: Switzerland
ID NLM: 7512719
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
12
10
2020
accepted:
12
02
2021
pubmed:
13
3
2021
medline:
19
2
2022
entrez:
12
3
2021
Statut:
ppublish
Résumé
Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts. To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial. The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial. The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests. A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]). We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic. Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.
Sections du résumé
BACKGROUND
Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts.
OBJECTIVE
To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial.
DESIGN, SETTING, AND PARTICIPANTS
The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests.
RESULTS AND LIMITATIONS
A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]).
CONCLUSIONS
We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic.
PATIENT SUMMARY
Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.
Identifiants
pubmed: 33707112
pii: S0302-2838(21)00145-7
doi: 10.1016/j.eururo.2021.02.025
pmc: PMC8627688
mid: NIHMS1675860
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
20-31Subventions
Organisme : NCI NIH HHS
ID : U24 CA196172
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180821
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180858
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180863
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180820
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180794
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180867
Pays : United States
Organisme : NCI NIH HHS
ID : U10 CA180888
Pays : United States
Commentaires et corrections
Type : CommentIn
Type : CommentIn
Informations de copyright
Copyright © 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.
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