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Related Experiment Video

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Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During

Jesutofunmi A Fajemisin1,2, John M Bryant3, Payman G Saghand4

  • 1Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.

JCO Clinical Cancer Informatics
|January 24, 2025
PubMed
Summary

Radiomics analysis of daily CT scans using delta-radiomics can predict disease progression in adaptive radiotherapy. Incorporating auxiliary data, particularly from lung cancer patients, significantly improved prediction accuracy in this study.

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Area of Science:

  • Radiotherapy
  • Medical Imaging
  • Oncology

Background:

  • Adaptive radiotherapy adjusts treatment based on daily anatomical changes.
  • Radiomics extracts quantitative imaging features to potentially predict clinical outcomes.

Purpose of the Study:

  • To investigate if delta-radiomics, analyzing changes in tumor volume between treatment fractions, can predict disease progression.
  • To evaluate the impact of auxiliary datasets on the predictive performance of delta-radiomics models.

Main Methods:

  • 108 patients (90 internal, 18 external) receiving ablative radiotherapy were analyzed.
  • Delta features were calculated as ratios (F5/F1) or concatenations (F1||F5) of radiomic features from the first and last fractions.
  • Decision tree classifiers were trained and tested with and without auxiliary datasets.

Main Results:

  • Internal training showed that auxiliary lung and pancreatic datasets improved the Area Under the Receiver Operator Characteristic Curve (AUC-ROC) for the F1||F5 model.
  • The F5/F1 model with lung auxiliary data achieved the highest AUC-ROC of 0.65 ± 0.11 during internal training.
  • External validation demonstrated AUC-ROCs of 0.70 for the F5||F1 model and 0.60 for the F5/F1 model when using lung auxiliary data.

Conclusions:

  • Decision tree models derived from delta-radiomics provided explainable predictions on external data.
  • This approach represents a potential step towards biologically adaptive radiotherapy by leveraging radiomics for recurrence prediction.