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Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model.

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Machine learning models can predict local failure in prostate cancer patients undergoing re-irradiation. This approach uses radiomic and dosiomic features from CT, PET scans, and radiotherapy plans to identify patients at risk.

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dosiomicsmachine learningpartial prostate re-irradiationprostate cancerradiomicsradiotherapy

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Area of Science:

  • Oncology
  • Radiotherapy
  • Machine Learning
  • Medical Imaging

Background:

  • Prostate cancer recurrence after initial treatment is a clinical challenge.
  • Partial prostate re-irradiation is a treatment option for isolated local recurrence.
  • Predicting local failure is crucial for optimizing treatment strategies.

Purpose of the Study:

  • To develop a machine learning classifier for predicting local failure after partial prostate re-irradiation.
  • To utilize radiomic features from CT and PET scans, along with dosimetric data, for prediction.
  • To assess the performance of the machine learning model in a cohort of patients with recurrent prostate cancer.

Main Methods:

  • A monocentric dataset of 43 patients with recurrent prostate cancer was analyzed.
  • Patients received partial prostate re-irradiation using volumetric modulated arc therapy.
  • An ensemble machine learning pipeline, incorporating feature selection and data balancing, was employed.
  • Radiomic features from CT and PET, alongside biological effective dose (BED) distribution, were used as input.

Main Results:

  • Local failure occurred in 13 out of 43 patients (30%).
  • A four-variable ensemble machine learning model achieved an accuracy of 0.62 and an AUC of 0.65.
  • The model demonstrated the capability to predict local failure based on radiomic and dosimetric features.

Conclusions:

  • Machine learning, particularly using dosimetric and radiomic features, can effectively predict local failure after partial prostate re-irradiation.
  • This predictive capability can aid in tailoring treatment and managing recurrent prostate cancer.
  • Further validation in larger cohorts is warranted to confirm these findings.