title: Spatially fractionated radiotherapy (GRID) using helical tomotherapy.
abstract: Spatially fractionated radiotherapy (GRID) was designed to treat large tumors while sparing skin, and it is usually delivered with a linear accelerator using a commercially available block or multileaf collimator (LINAC-GRID). For deep-seated (skin to tumor distance (> 8 cm)) tumors, it is always a challenge to achieve adequate tumor dose coverage. A novel method to perform GRID treatment using helical tomotherapy (HT-GRID) was developed at our institution. Our approach allows treating patients by generating a patient-specific virtual GRID block (software-generated) and using IMRT technique to optimize the treatment plan. Here, we report our initial clinical experience using HT-GRID, and dosimetric comparison results between HT-GRID and LINAC-GRID. This study evaluates 10 previously treated patients who had deep-seated bulky tumors with complex geometries. Five of these patients were treated with HT-GRID and replanned with LINAC-GRID for comparison. Similarly, five other patients were treated with LINAC-GRID and replanned with HT-GRID for comparison. The prescription was set such that the maximum dose to the GTV is 20 Gy in a single fraction. Dosimetric parameters compared included: mean GTV dose (DGTV mean), GTV dose inhomogeneity (valley-to-peak dose ratio (VPR)), normal tissue doses (DNmean), and other organs-at-risk (OARs) doses. In addition, equivalent uniform doses (EUD) for both GTV and normal tissue were evaluated. In summary, HT-GRID technique is patient-specific, and allows adjustment of the GRID pattern to match different tumor sizes and shapes when they are deep-seated and cannot be adequately treated with LINAC-GRID. HT-GRID delivers a higher DGTV mean, EUD, and VPR compared to LINAC-GRID. HT-GRID delivers a higher DNmean and lower EUD for normal tissue compared to LINAC-GRID. HT-GRID plans also have more options for tumors with complex anatomical relationships between the GTV and the avoidance OARs (abutment or close proximity).
authors.affiliation: University of Arkansas for Medical Science. xinzhang2003@gmail.com.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.forename: Jose
authors.initials: J
authors.lastname: Penagaricano
authors.forename: Yulong
authors.initials: Y
authors.lastname: Yan
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Liang
authors.forename: Steven
authors.initials: S
authors.lastname: Morrill
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Griffin
authors.forename: Peter
authors.initials: P
authors.lastname: Corry
authors.forename: Vaneerat
authors.initials: V
authors.lastname: Ratanatharathorn
mesh_terms: D003198:Computer Simulation; D019583:Dose Fractionation, Radiation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D059351:Organ Sparing Treatments; D058958:Organs at Risk / Q000528:radiation effects; D010315:Particle Accelerators; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated; D012984:Software; D036542:Tomography, Spiral Computed / Q000379:methods*