Abstract
PURPOSE: This R-Ideal stage 1b/2a study describes the workflow and feasibility of long-course fractionated online adaptive MR-guided chemoradiotherapy with reduced CTV-to-PTV margins on the 1.5T MR-Linac for patients with esophageal cancer. METHODS: Patients with esophageal cancer scheduled to undergo chemoradiation were treated on a 1.5T MR-Linac. Daily MR-images were acquired for online contour adaptation and replanning. Contours were manually adapted to match the daily anatomy and an isotropic CTV-to-PTV margin of 6 mm was applied. Time was recorded for all individual steps in the workflow. Feasibility and patient tolerability were defined as on-table time of ≤60 min and completion of >95% of the fractions on the MR-Linac, respectively. Positioning verification and post-treatment MRIs were retrospectively analyzed and dosimetric parameters were compared to standard non-adaptive conventional treatment plans. RESULTS: Nine patients with esophageal cancer were treated with chemoradiation; eight patients received 41.4 Gy in 23 fractions and one received 50.4 Gy in 28 fractions. Four patients received all planned fractions on the MR-Linac, whereas for two patients >5% of fractions were rescheduled to a conventional linac for reasons of discomfort. A total of 183 (86%) of 212 scheduled fractions were successfully delivered on the MR-Linac. Three fractions ended prematurely due to technical issues and 26 fractions were rescheduled on a conventional linac due to MR-Linac downtime (n = 10), logistical reasons (n = 3) or discomfort (n = 13).The median time per fraction was 53 min (IQR = 3 min). Daily adapted MR-Linac plans had similar target coverage, whereas dose to the organs-at-risk was significantly reduced compared to conventional treatment (26% and 12% reduction in mean lung and heart dose, respectively). CONCLUSION: Daily online adaptive fractionated chemoradiotherapy with reduced PTV margins is moderately feasible for esophageal cancer and results in better sparing of heart and lungs. Future studies should focus on further optimization and acceleration of the current workflow.