TITLE:
Automated Heuristic Optimization of Prostate VMAT Treatment Planning
AUTHORS:
Christian Fiandra, Alessandro Alparone, Elena Gallio, Claudio Vecchi, Gabriella Balestra, Sara Bartoncini, Samanta Rosati, Riccardo Ragona, Umberto Ricardi
KEYWORDS:
Genetic Algorithm Optimization, Planning Automation, VMAT, Prostate
JOURNAL NAME:
International Journal of Medical Physics, Clinical Engineering and Radiation Oncology,
Vol.7 No.3,
August
31,
2018
ABSTRACT: Purpose: To
investigate a genetic algorithm approach to automatic treatment planning. Methods: A Python script based on genetic algorithm (GA) was implemented for VMAT
treatment planning of prostate tumor. The script was implemented in RayStation
treatment planning system using Python code. Two different clinical
prescriptions were considered: 78 Gy prescribed to planning target volume in 39
fractions (GROUP 1) and simultaneous integrated boost (70.2 Gy to prostate bed
and 61.1 Gy to seminal vesicles) in 26 fractions (GROUP 2). The script
automatically optimizes doses to PTV and OARs according to GA. A comparison
with corresponding plans created with Monaco TPS (M) and Auto-Planning module
of Pinnacle3 (AP) was carried out. The plans were evaluated with a
total score (TS) of PlanIQ software in terms of target coverage and sparing of
OARs as well as clinical score (CS) performed by a Radiation Oncologist. Results: In GROUP 1, mean value of TS were 150.6 ± 30.7, 146.3 ± 36.1 and 137.4 ± 35.7
for AP, GA and M respectively. For GROUP 2, mean value for TS were 163.5 ± 16.8,
163.4 ± 24.7 and 162.9 ± 16.6 for AP, GA and M respectively with no
significance differences. In terms of CS, the highest value has been attributed
to GA in four patients out of five for both GROUP 1 and 2. Conclusions: Genetic approach is practicable for prostate VMAT plan generation and
studies are underway in other anatomical sites such as Head and Neck and
Rectum.