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Related Experiment Video

Updated: May 2, 2026

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
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Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

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Time-Dynamic AI Models to Predict Quality of Life in Patients With Breast Cancer: Development and Validation Study

Niclas J Hubel1, Thijs G W van der Heijden2, Benjamin Murauer3

  • 1Health Outcomes Research Unit, University Hospital of Psychiatry II, Medical University of Innsbruck, Anichstrasse 35, Innsbruck, 6020, Austria, 00 43 0 512 5042 3629.

Journal of Medical Internet Research
|April 30, 2026
PubMed
Summary

Machine learning models can predict health-related quality of life (HRQoL) impairments in breast cancer patients. These time-dynamic models offer personalized predictions, improving survivorship care and clinical follow-up.

Keywords:
HRQoLbreast cancerhealth-related quality of lifemachine learningpatient-reported outcomesprediction modeling

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Area of Science:

  • Oncology
  • Health Informatics
  • Machine Learning

Background:

  • Breast cancer patients frequently experience unpredictable health-related quality of life (HRQoL) impairments.
  • Current prognostic models have limitations due to fixed intervals, hindering personalized clinical follow-up.

Purpose of the Study:

  • To develop and externally validate time-dynamic machine learning (ML) models for predicting HRQoL impairments in nonmetastatic breast cancer patients.
  • To assess the utility of ML in understanding individual survivorship trajectories and improving clinical decision-making.

Main Methods:

  • Utilized the EORTC BALANCE dataset (n=6316) with repeated HRQoL measurements (EORTC QLQ-C30).
  • Trained ML algorithms on prior HRQoL and clinical data to predict impairments 2 weeks to 5 years ahead.
  • Externally validated models on an independent cohort, evaluating performance across various patient risk groups.

Main Results:

  • ML models demonstrated good discrimination (AUC 0.64-0.84) for most HRQoL domains, particularly fatigue and functioning.
  • Gradient boosting models performed best but showed overconfidence and calibration issues for low-prevalence symptoms.
  • Model performance varied by risk group, with prior HRQoL being the strongest predictor, outperforming clinical variables.

Conclusions:

  • Time-dynamic ML models offer potential for personalized HRQoL prediction in breast cancer care.
  • Future research should prioritize model calibration and fairness for equitable clinical implementation.