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Machine learning-based automated planning for hippocampal avoidance prophylactic cranial irradiation.

Rodríguez de Dios N1,2,3, Martínez Moñino A4, Cristina Liu4

  • 1Department of Radiation Oncology, Hospital del Mar, Passeig Marítim de la Barceloneta, 25-29, 08003, Barcelona, Spain. nrodriguez@psmar.cat.

Clinical & Translational Oncology : Official Publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
|October 4, 2022
PubMed
Summary

Automated treatment planning for hippocampal avoidance prophylactic cranial irradiation (HA-PCI) using a knowledge-based model significantly reduced planning time and improved plan quality. This AI-driven approach enhances efficiency and safety in radiotherapy.

Keywords:
Artificial intelligenceHippocampal avoidanceKnowledge basedMachine learningProphylactic cranial irradiation

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Prophylactic cranial irradiation (PCI) is used in small-cell lung cancer treatment.
  • Hippocampal avoidance (HA) is crucial to mitigate neurocognitive side effects.
  • Automated treatment planning can improve efficiency and consistency.

Purpose of the Study:

  • To design and evaluate a knowledge-based model for automated hippocampal avoidance prophylactic cranial irradiation (HA-PCI) planning.
  • To assess the efficiency and clinical acceptability of AI-generated HA-PCI plans.
  • To compare automated plans with manually generated plans.

Main Methods:

  • A knowledge-based (KB) planning routine was developed using commercial AI tools.
  • Data from 44 patients were used for training, with a second version validated on 9 additional plans.
  • Automated plans (AP) were compared to manual plans (MP) using PREMER trial objectives for 10 test patients.

Main Results:

  • Automated planning reduced optimization time by 55% (15 vs 33 min).
  • AP showed statistically significant improvements in D98%, Homogeneity Index, and Hippocampus Dmean compared to MP.
  • AP met objectives without deviation, while MP had deviations in 2 cases.

Conclusions:

  • A knowledge-based model enables automated HA-PCI planning.
  • Automation of radiotherapy planning enhances efficiency, safety, and quality.
  • This approach may improve access to advanced radiotherapy techniques.