title: Automatic learning-based beam angle selection for thoracic IMRT.
abstract: OBJECTIVE
  The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose-volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner's clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload.
  
  
  METHODS
  The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth.
  
  
  RESULTS
  The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume coverage and organ at risk sparing and were superior over plans produced with fixed sets of common beam angles. The great majority of the automatic plans (93%) were approved as clinically acceptable by three radiation therapy specialists.
  
  
  CONCLUSIONS
  The results demonstrated the feasibility of utilizing a learning-based approach for automatic selection of beam angles in thoracic IMRT planning. The proposed method may assist in reducing the manual planning workload, while sustaining plan quality.
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada.
authors.forename: Guy
authors.initials: G
authors.lastname: Amit
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2, Canada; and Techna Institute, University Health Network, Toronto, Ontario M5G 1P5, Canada.
authors.forename: Thomas G
authors.initials: TG
authors.lastname: Purdie
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada.
authors.forename: Alex
authors.initials: A
authors.lastname: Levinshtein
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2, Canada.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Hope
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2, Canada.
authors.forename: Patricia
authors.initials: P
authors.lastname: Lindsay
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada.
authors.forename: Andrea
authors.initials: A
authors.lastname: Marshall
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2, Canada; and Techna Institute, University Health Network, Toronto, Ontario M5G 1P5, Canada.
authors.forename: David A
authors.initials: DA
authors.lastname: Jaffray
authors.affiliation: Philips Healthcare, Markham, Ontario L6C 2S3, Canada.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Pekar
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012044:Regression Analysis; D013909:Thorax / Q000033:anatomy & histology / Q000473:pathology / Q000528:radiation effects*; D014057:Tomography, X-Ray Computed