title: Linear energy transfer-guided optimization in intensity modulated proton therapy: feasibility study and clinical potential.
abstract: OBJECTIVE
  To investigate the feasibility and potential clinical benefit of linear energy transfer (LET) guided plan optimization in intensity modulated proton therapy (IMPT).
  
  
  METHODS
  A multicriteria optimization (MCO) module was used to generate a series of Pareto-optimal IMPT base plans (BPs), corresponding to defined objectives, for 5 patients with head-and-neck cancer and 2 with pancreatic cancer. A Monte Carlo platform was used to calculate dose and LET distributions for each BP. A custom-designed MCO navigation module allowed the user to interpolate between BPs to produce deliverable Pareto-optimal solutions. Differences among the BPs were evaluated for each patient, based on dose-volume and LET-volume histograms and 3-dimensional distributions. An LET-based relative biological effectiveness (RBE) model was used to evaluate the potential clinical benefit when navigating the space of Pareto-optimal BPs.
  
  
  RESULTS
  The mean LET values for the target varied up to 30% among the BPs for the head-and-neck patients and up to 14% for the pancreatic cancer patients. Variations were more prominent in organs at risk (OARs), where mean LET values differed by a factor of up to 2 among the BPs for the same patient. An inverse relation between dose and LET distributions for the OARs was typically observed. Accounting for LET-dependent variable RBE values, a potential improvement on RBE-weighted dose of up to 40%, averaged over several structures under study, was noticed during MCO navigation.
  
  
  CONCLUSIONS
  We present a novel strategy for optimizing proton therapy to maximize dose-averaged LET in tumor targets while simultaneously minimizing dose-averaged LET in normal tissue structures. MCO BPs show substantial LET variations, leading to potentially significant differences in RBE-weighted doses. Pareto-surface navigation, using both dose and LET distributions for guidance, provides the means for evaluating a large variety of deliverable plans and aids in identifying the clinically optimal solution.
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts, USA. dgiantsoudi@partners.org
authors.forename: Drosoula
authors.initials: D
authors.lastname: Giantsoudi
authors.forename: Clemens
authors.initials: C
authors.lastname: Grassberger
authors.forename: David
authors.initials: D
authors.lastname: Craft
authors.forename: Andrzej
authors.initials: A
authors.lastname: Niemierko
authors.forename: Alexei
authors.initials: A
authors.lastname: Trofimov
authors.forename: Harald
authors.initials: H
authors.lastname: Paganetti
mesh_terms: D002817:Chordoma / Q000000981:diagnostic imaging / Q000473:pathology / Q000532:radiotherapy; D005240:Feasibility Studies; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000532:radiotherapy*; D006801:Humans; D018499:Linear Energy Transfer; D009010:Monte Carlo Method; D058958:Organs at Risk / Q000000981:diagnostic imaging / Q000528:radiation effects*; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D061766:Proton Therapy / Q000379:methods*; D011859:Radiography; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012062:Relative Biological Effectiveness