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Related Concept Videos

Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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A Novel Method for Prognostic Risk Classification After Carbon-Ion Radiotherapy for Hepatocellular Carcinoma Using

Kazuhiko Hayashi1,2, Osamu Suzuki1, Koji Ichise1

  • 1Department of Radiology, Osaka Heavy Ion Therapy Center, Osaka, Japan.

Cancer Science
|May 25, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a new risk classification for cancer-specific survival (CSS) after carbon-ion radiotherapy for hepatocellular carcinoma. Decision tree analysis identified key factors, enabling stratification into low, intermediate, and high-risk groups.

Keywords:
carbon‐ion radiotherapydata‐mining methoddecision tree analysishepatocellular carcinomaprognosis

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Area of Science:

  • Oncology
  • Radiotherapy
  • Data Mining

Background:

  • Hepatocellular carcinoma (HCC) treatment lacks established prognostic models for carbon-ion radiotherapy.
  • Predicting cancer-specific survival (CSS) is crucial for optimizing patient management.

Purpose of the Study:

  • To develop a risk classification system for CSS following carbon-ion radiotherapy in HCC patients.
  • To utilize decision tree analysis (DTA) for identifying significant prognostic factors.

Main Methods:

  • Retrospective analysis of 90 HCC patients treated with carbon-ion radiotherapy (2018-2022).
  • Univariate, multivariate analyses, and DTA were employed to identify prognostic factors for CSS and progression-free survival (PFS).
  • Irradiation doses were standardized at 60 Gy (relative biological effectiveness [RBE]) with varied fractionation based on tumor proximity to the gastrointestinal tract.

Main Results:

  • Multivariate analysis identified dose fractionation and pretreatment alpha-fetoprotein levels as significant prognostic factors for PFS and CSS.
  • Clinical stage and pretreatment protein induced by vitamin K absence or antagonist II values were significant prognostic factors for CSS.
  • DTA successfully stratified patients into low-risk (100% 3-year CSS), intermediate-risk (73.3% 3-year CSS), and high-risk (44.4% 3-year CSS) groups.

Conclusions:

  • Decision tree analysis offers a novel approach for risk stratification in HCC patients undergoing carbon-ion radiotherapy.
  • The developed classification system, based on tumor markers and clinical stage, can aid in predicting CSS.
  • This method provides a foundation for personalized treatment strategies and improved patient outcomes.