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Related Experiment Video

Updated: Oct 6, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Risk-group Classification by Recursive Partitioning Analysis of Patients Affected by Oligometastatic Renal Cancer

C Franzese1, P Navarria2, L Bellu2

  • 1Department of Biomedical Sciences, Humanitas University, Milan, Italy; IRCCS Humanitas Research Hospital, Department of Radiotherapy and Radiosurgery, Milan, Italy.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|January 14, 2022
PubMed
Summary

Stereotactic radiotherapy (SRT) for oligometastatic kidney cancer effectively stratifies patients into four survival risk groups. Key factors include age, bone disease, and metastasis location, aiding treatment decisions.

Keywords:
OligometastasesRadiosurgeryRenal cell carcinomaSBRTSRS

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Area of Science:

  • Oncology
  • Radiation Oncology
  • Medical Physics

Background:

  • Consensus on metastases-directed treatment for kidney cancer is lacking.
  • Stereotactic radiotherapy (SRT) is increasingly used for limited metastatic disease.

Purpose of the Study:

  • To classify kidney cancer patients treated with SRT for metastases into survival risk groups.
  • To identify pre-treatment characteristics that predict survival outcomes.

Main Methods:

  • Analysis of 129 oligometastatic kidney cancer patients treated with SRT on up to five metastases.
  • Recursive partitioning analysis (RPA) applied to stratify patients based on overall survival.
  • Overall survival and prognostic classes were determined.

Main Results:

  • Four distinct prognostic classes were identified using RPA.
  • Class 1 (younger, extracranial metastases) had 3-year survival of 82.66%.
  • Class 4 (brain metastases) had 3-year survival of 9.70%.

Conclusions:

  • A stratification model predicts survival for oligometastatic kidney cancer patients receiving SRT.
  • Patient age, bone disease status, and metastasis site are crucial for clinical decision-making.