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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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An Integrated Clinical-MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer.

Xingfeng Li1, Diana Marcus1,2, James Russell3

  • 1Department of Surgery and Cancer, Imperial College, London, UK.

Journal of Magnetic Resonance Imaging : JMRI
|December 9, 2022
PubMed
Summary

Predicting survival time for women with endometrial cancer is improved by integrating clinical data with MRI-based radiomic features. This new model enhances survival estimation for better treatment planning in endometrial cancer patients.

Keywords:
Cox proportional hazards modelT2-weighted MRIendometrial cancerfeature selectionradiomicssurvival analysis

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Survival time prediction in endometrial cancer using clinical features is currently imprecise.
  • Magnetic Resonance Imaging (MRI) features may offer improved survival estimation for enhanced treatment planning.

Purpose of the Study:

  • To identify clinical features and T2-weighted MRI imaging signatures for an integrated model.
  • To estimate survival time in endometrial cancer patients.

Main Methods:

  • Retrospective study with 413 endometrial cancer patients (training, validation, and testing sets).
  • Tumor segmentation on T2-weighted MRI, feature extraction, and inclusion of clinical variables (age, histologic grade, risk score).
  • Cox proportional hazards (CPH) model with a least absolute shrinkage and selection operator (LASSO) method.

Main Results:

  • An integrated model incorporating three radiomic features and two clinical variables (age, cancer grade) was developed.
  • The integrated model showed improved predictive performance (Concordance Index [CI] 0.818, Area Under the Curve [AUC] 0.853 on validation data) compared to the clinical model (CI 0.797, AUC 0.805).
  • Testing dataset confirmed improved performance (CI 0.882, AUC 0.727) for the integrated model versus the clinical model (CI 0.792, AUC 0.624).

Conclusions:

  • The proposed Cox proportional hazards (CPH) model, utilizing radiomic signatures, can enhance survival time estimation in women with endometrial cancer.
  • This approach may serve as a valuable tool for improving treatment planning and patient outcomes.