title: Novel tools for stepping source brachytherapy treatment planning: enhanced geometrical optimization and interactive inverse planning.
abstract: OBJECTIVE
  Dose optimization for stepping source brachytherapy can nowadays be performed using automated inverse algorithms. Although much quicker than graphical optimization, an experienced treatment planner is required for both methods. With automated inverse algorithms, the procedure to achieve the desired dose distribution is often based on trial-and-error.
  
  
  METHODS
  A new approach for stepping source prostate brachytherapy treatment planning was developed as a quick and user-friendly alternative. This approach consists of the combined use of two novel tools: Enhanced geometrical optimization (EGO) and interactive inverse planning (IIP). EGO is an extended version of the common geometrical optimization method and is applied to create a dose distribution as homogeneous as possible. With the second tool, IIP, this dose distribution is tailored to a specific patient anatomy by interactively changing the highest and lowest dose on the contours.
  
  
  RESULTS
  The combined use of EGO-IIP was evaluated on 24 prostate cancer patients, by having an inexperienced user create treatment plans, compliant to clinical dose objectives. This user was able to create dose plans of 24 patients in an average time of 4.4 min/patient. An experienced treatment planner without extensive training in EGO-IIP also created 24 plans. The resulting dose-volume histogram parameters were comparable to the clinical plans and showed high conformance to clinical standards.
  
  
  CONCLUSIONS
  Even for an inexperienced user, treatment planning with EGO-IIP for stepping source prostate brachytherapy is feasible as an alternative to current optimization algorithms, offering speed, simplicity for the user, and local control of the dose levels.
authors.affiliation: Department of Radiation Oncology, Academic Medical Center Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands.
authors.forename: Anna M
authors.initials: AM
authors.lastname: Dinkla
authors.affiliation: Department of Radiation Oncology, Academic Medical Center Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands.
authors.forename: Rob
authors.initials: R
authors.lastname: van der Laarse
authors.affiliation: Department of Radiation Oncology, Academic Medical Center Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands.
authors.forename: Kees
authors.initials: K
authors.lastname: Koedooder
authors.affiliation: Department of Radiation Oncology, Academic Medical Center Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands.
authors.forename: H
authors.initials: H
authors.lastname: Petra Kok
authors.affiliation: Department of Radiation Oncology, Academic Medical Center Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands.
authors.forename: Niek
authors.initials: N
authors.lastname: van Wieringen
authors.affiliation: Department of Radiation Oncology, Academic Medical Center Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands.
authors.forename: Bradley R
authors.initials: BR
authors.lastname: Pieters
authors.affiliation: Department of Radiation Oncology, Academic Medical Center Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands.
authors.forename: Arjan
authors.initials: A
authors.lastname: Bel
mesh_terms: D000465:Algorithms; D001918:Brachytherapy / Q000379:methods*; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D013997:Time Factors