title: Models for predicting objective function weights in prostate cancer IMRT.
abstract: OBJECTIVE
  To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer.
  
  
  METHODS
  A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated.
  
  
  RESULTS
  The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics.
  
  
  CONCLUSIONS
  The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs.
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada.
authors.forename: Justin J
authors.initials: JJ
authors.lastname: Boutilier
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada.
authors.forename: Taewoo
authors.initials: T
authors.lastname: Lee
authors.affiliation: Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada and Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2, Canada.
authors.forename: Tim
authors.initials: T
authors.lastname: Craig
authors.affiliation: Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada; Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2, Canada; and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5, Canada.
authors.forename: Michael B
authors.initials: MB
authors.lastname: Sharpe
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5, Canada.
authors.forename: Timothy C Y
authors.initials: TC
authors.lastname: Chan
mesh_terms: D066264:Datasets as Topic; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D058958:Organs at Risk / Q000528:radiation effects; D017785:Photons / Q000627:therapeutic use; D011467:Prostate / Q000528:radiation effects; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012007:Rectum / Q000528:radiation effects; D012189:Retrospective Studies; D001743:Urinary Bladder / Q000528:radiation effects