title: Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy.
abstract: This work presents the application of a machine learning (ML) algorithm to automatically generate high-quality, prostate low-dose-rate (LDR) brachytherapy treatment plans. The ML algorithm can mimic characteristics of preoperative treatment plans deemed clinically acceptable by brachytherapists. The planning efficiency, dosimetry, and quality (as assessed by experts) of preoperative plans generated with an ML planning approach was retrospectively evaluated in this study.
  
  
  
  Preimplantation and postimplantation treatment plans were extracted from 100 high-quality LDR treatments and stored within a training database. The ML training algorithm matches similar features from a new LDR case to those within the training database to rapidly obtain an initial seed distribution; plans were then further fine-tuned using stochastic optimization. Preimplantation treatment plans generated by the ML algorithm were compared with brachytherapist (BT) treatment plans in terms of planning time (Wilcoxon rank sum, α = 0.05) and dosimetry (1-way analysis of variance, α = 0.05). Qualitative preimplantation plan quality was evaluated by expert LDR radiation oncologists using a Likert scale questionnaire.
  
  
  
  The average planning time for the ML approach was 0.84 ± 0.57 minutes, compared with 17.88 ± 8.76 minutes for the expert planner (P=.020). Preimplantation plans were dosimetrically equivalent to the BT plans; the average prostate V150% was 4% lower for ML plans (P=.002), although the difference was not clinically significant. Respondents ranked the ML-generated plans as equivalent to expert BT treatment plans in terms of target coverage, normal tissue avoidance, implant confidence, and the need for plan modifications. Respondents had difficulty differentiating between plans generated by a human or those generated by the ML algorithm.
  
  
  
  Prostate LDR preimplantation treatment plans that have equivalent quality to plans created by brachytherapists can be rapidly generated using ML. The adoption of ML in the brachytherapy workflow is expected to improve LDR treatment plan uniformity while reducing planning time and resources.
authors.affiliation: Department of Physics, Ryerson University, Toronto, Ontario, Canada; Department of Medical Physics, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
authors.forename: Alexandru
authors.initials: A
authors.lastname: Nicolae
authors.affiliation: Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
authors.forename: Gerard
authors.initials: G
authors.lastname: Morton
authors.affiliation: Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
authors.forename: Hans
authors.initials: H
authors.lastname: Chung
authors.affiliation: Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
authors.forename: Andrew
authors.initials: A
authors.lastname: Loblaw
authors.affiliation: Department of Clinical Oncology, The Northern Ireland Cancer Centre, Belfast City Hospital, Antrim, Northern Ireland, UK.
authors.forename: Suneil
authors.initials: S
authors.lastname: Jain
authors.affiliation: Department of Clinical Oncology, The Northern Ireland Cancer Centre, Belfast City Hospital, Antrim, Northern Ireland, UK.
authors.forename: Darren
authors.initials: D
authors.lastname: Mitchell
authors.affiliation: Department of Radiation Therapy, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
authors.forename: Lin
authors.initials: L
authors.lastname: Lu
authors.affiliation: Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
authors.forename: Joelle
authors.initials: J
authors.lastname: Helou
authors.affiliation: Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
authors.forename: Motasem
authors.initials: M
authors.lastname: Al-Hanaqta
authors.affiliation: Department of Physics, Carleton University, Ottawa, Ontario, Canada.
authors.forename: Emily
authors.initials: E
authors.lastname: Heath
authors.affiliation: Department of Medical Physics, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. Electronic address: ananth.ravi@sunnybrook.ca.
authors.forename: Ananth
authors.initials: A
authors.lastname: Ravi
mesh_terms: D000465:Algorithms; D001918:Brachytherapy / Q000379:methods*; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D000069550:Machine Learning; D008297:Male; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D061089:Radiotherapy, Image-Guided / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome