Development of an automated treatment planning approach for lattice radiation therapy

• DOI: 10.1002/mp.16761
• Publication Date: 2023-10-06T17:26:17Z
• Authors: Mathieu Gaudreault; David Chang; Tomas Kron; Shankar Siva; Sarat Chander; Nicholas Hardcastle; Adam Yeo
• Keywords: LRT; ESAPI; sarcoma; Radiotherapy Planning, Computer-Assisted; Neoplasms; SFRT; plan optimization; Humans; Radiotherapy Dosage; Radiotherapy, Conformal; 3. Good health; automation

Abstract Background Lattice radiation therapy (LRT) alternates regions of high and low doses within the target. The heterogeneous dose distribution is delivered to a geometrical structure vertices segmented inside tumor. LRT typically used treat patients with large tumor volumes cytoreduction intent. Due geometric complexity target volume required distribution, treatment planning demands additional resources, which may limit clinical integration. Purpose We introduce fully automated method (1) generate an ordered lattice various sizes center‐to‐center distances (2) perform optimization calculation. aim report dosimetry associated these lattices help decision‐making. Methods Sarcoma cancer between 100 cm 3 1500 who received radiotherapy 2010 2018 at our institution were considered for inclusion. Automated segmentation optimization/calculation performed by using Eclipse Scripting Application Programming Interface (ESAPI, v16, Varian Medical Systems, Palo Alto, USA). Vertices modeled spheres gross (GTV) 1 cm/1.5 cm/2 diameters (LRT‐1 cm) 2 5 distance on square alternating along superior‐inferior direction. Organs risk subtracting GTV from body (body‐GTV). prescription was that 50% vertice should receive least 20 Gy in one fraction. included three stages. objectives refined during according their values end first second Lattices classified score based minimization body‐GTV max maximization uniformity (measured equivalent uniform [EUD]), heterogeneity D90%/D10% ratio), number more than vertex inserted GTV. Plan measured modulation (MCS). Correlations assessed Spearman correlation coefficient (r) its p‐value. Results Thirty‐three 150 1350 (median = 494 , IQR 272–779 included. median time segmentation/planning min/21 min. strongly correlated each (r > 0.85, p ‐values < 0.001 case). 2.5 cm/3 cm/3.5 LRT‐1.5 4 LRT‐1 had best scores. These characterized 0.06 0.19). generated plans moderately complex MCS ranged 0.19 0.40). Conclusions allows efficacious generation arranged refinement optimization, enabling systematic evaluation geometries.