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Nicolae A, Morton G, Chung H, Loblaw A, Jain S, Mitchell D, Lu L, Helou J, Al-Hanaqta M, Heath E, Ravi A. Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy. Int J Radiat Oncol Biol Phys 2016; 97:822-829. [PMID: 28244419 DOI: 10.1016/j.ijrobp.2016.11.036] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 11/01/2016] [Accepted: 11/08/2016] [Indexed: 10/20/2022]
Abstract
PURPOSE 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. METHODS AND MATERIALS 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. RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- Alexandru Nicolae
- Department of Physics, Ryerson University, Toronto, Ontario, Canada; Department of Medical Physics, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Gerard Morton
- Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Hans Chung
- Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Andrew Loblaw
- Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Suneil Jain
- Department of Clinical Oncology, The Northern Ireland Cancer Centre, Belfast City Hospital, Antrim, Northern Ireland, UK
| | - Darren Mitchell
- Department of Clinical Oncology, The Northern Ireland Cancer Centre, Belfast City Hospital, Antrim, Northern Ireland, UK
| | - Lin Lu
- Department of Radiation Therapy, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Joelle Helou
- Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Motasem Al-Hanaqta
- Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Emily Heath
- Department of Physics, Carleton University, Ottawa, Ontario, Canada
| | - Ananth Ravi
- Department of Medical Physics, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
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