Automation of pencil beam scanning proton treatment planning for intracranial tumours

Published:December 17, 2022DOI:


      • Comprehensive automation of intra-cranial proton treatment planning system.
      • Automatic beams’ geometry selection based on intra-cranial target localization.
      • Beams’ geometry derived from previous planning experience and heterogeneity index.
      • Wish list approach is employed to a benchmark dose distribution for plan optimization.
      • Feasibility of the system in terms of time to generate clinical plans within OARs clinical constrains.



      To evaluate the feasibility of comprehensive automation of an intra-cranial proton treatment planning.

      Materials and methods

      Class solution (CS) beam configuration selection allows the user to identify predefined beam configuration based on target localization; automatic CS (aCS) will then explore all the possible CS beam geometries. Ten patients, already used for the evaluation of the automatic selection of the beam configuration, have been also employed to training an algorithm based on the computation of a benchmark dose exploit automatic general planning solution (GPS) optimization with a wish list approach for the planning optimization. An independent cohort of ten patients has been then used for the evaluation step between the clinical and the GPS plan in terms of dosimetric quality of plans and the time needed to generate a plan.


      The definition of a beam configuration requires on average 22 min (range 9–29 min).
      The average time for GPS plan generation is 18 min (range 7–26 min). Median dose differences (GPS-Manual) for each OAR constraints are: brainstem −1.60 Gy, left cochlea −1.22 Gy, right cochlea −1.42 Gy, left eye 0.55 Gy, right eye −2.33 Gy, optic chiasm −1.87 Gy, left optic nerve −4.45 Gy, right optic nerve −2.48 Gy and optic tract −0.31 Gy. Dosimetric CS and aCS plan evaluation shows a slightly worsening of the OARs values except for the optic tract and optic chiasm for both CS and aCS, where better results have been observed.


      This study has shown the feasibility and implementation of the automatic planning system for intracranial tumors. The method developed in this work is ready to be implemented in a clinical workflow.


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