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Cited by 675

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Risk Group Assessment and Clinical Outcome Algorithm to Predict the Natural History of Patients With Surgically Resected Renal Cell Carcinoma

Zisman, Amnon, Pantuck, Allan J., Wieder, Jeffery, Chao, Debby H., Dorey, Fredrick, Said, Jonathan W., deKernion, Jean B., Figlin, Robert A., Belldegrun, Arie S.·2002·Journal of Clinical Oncology

Abstract

PURPOSE: To create a comprehensive algorithm that can predict postoperative renal cell carcinoma (RCC) patient outcomes and response to therapy. PATIENTS AND METHODS: A prospective cohort study was performed with outcome assessment on the basis of chart review of 814 patients who underwent nephrectomy between 1989 and 2000. At diagnosis, M1 or N1/N2M0 metastatic disease (M) was present in 346 patients (43%), whereas 468 patients had no metastatic disease (NM) (N0M0). On the basis of UCLA Integrated Staging System category and the presence of metastases, patients were divided into low-risk (LR), intermediate-risk (IR), and high-risk (HR) groups. Decision boxes integrating tumor-node-metastasis staging, tumor grade, and performance status were compiled for determining a patient’s risk group. RESULTS: NM-LR patients had 91% disease-specific survival at 5 years, lower recurrence rate, and better disease-free survival compared with NM-IR and HR patients. Disease progressed in 50% of NM-HR patients. Disease-specific survival of NM-HR patients who received immunotherapy (IMT) for recurrent disease was similar to that of M-LR patients treated with cytoreductive nephrectomy and adjuvant IMT. Time from recurrence to death for NM-HR patients was inferior to that for M-LR patients. After IMT, approximately 25% of M-LR and 12% of M-IR patients had long-term progression-free survival. M-HR patients did poorly despite IMT. CONCLUSION: Stratifying RCC patients into high-, intermediate-, and low-risk subgroups provides a clinically useful system for predicting outcome and provides a unique tool for risk assignment and outcome analysis. Subclassifying RCC into well-defined risk groups should allow better patient counseling and identification of both NM-HR subgroups that need adjuvant treatment and nonresponders who need alternative therapies.

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Metadata

Authors
Zisman, A., Pantuck, A. J., Wieder, J., Chao, D. H., Dorey, F., Said, J. W., deKernion, J. B., Figlin, R. A., & Belldegrun, A. S.
Venue
Journal of Clinical Oncology
Published
2002-12-01
Volume
20
Issue
23
Pages
4559-4566
ISSN
  • 0732-183X (ISSN-L)
  • 1527-7755
OpenAlex
W2083887116
Cited by
675
References
9
FWCI
14.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Risk Group Assessment and Clinical Outcome Algorithm to Predict the Natural History of Patients With Surgically Resected Renal Cell Carcinoma. https://app.cochat.ai/papers/10.1200/jco.2002.05.111
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Zisman, A., Pantuck, A. J., Wieder, J., Chao, D. H., Dorey, F., Said, J. W., deKernion, J. B., Figlin, R. A., & Belldegrun, A. S. (2002). Risk Group Assessment and Clinical Outcome Algorithm to Predict the Natural History of Patients With Surgically Resected Renal Cell Carcinoma. Journal of Clinical Oncology. https://doi.org/10.1200/jco.2002.05.111

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.