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Published on: January 29, 2019
Apparent diffusion coefficient increases during short course radiotherapy in rectal tumours: Results from a
Anne L H Bisgaard1, Chavelli M Kensen2, Marielle E P Philippens3
1Laboratory of Radiation Physics, Odense University Hospital, Odense, Kløvervænget 19, 5000 Odense, Denmark.
Background And Purpose:
The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI), a form of magnetic resonance imaging (MRI), has shown promise for predicting response to long course neoadjuvant chemoradiotherapy in rectal cancer. This study investigated whether ADC changes are detectable during short course radiotherapy in patients with rectal cancer.
Materials And Methods:
Across 3 centres, this study included 138 patients with primary tumours, who received neoadjuvant short course radiotherapy (5 fractions of 5 Gy) on a 1.5 T MRI linear accelerator (MRI-linac), without any prior oncological treatments. DWI was acquired at each fraction prior to beam-on. ADC maps were calculated centrally using a mono-exponential model using b-values between 150-800 s/mm2. Median scaling of ADC voxel values was performed between two identified groups of DWI sequences. Tumours were semi-automatically delineated on DWI, and median ADCs were extracted per fraction. ADC time-trends over the course of radiotherapy were extracted using linear fitting, with 95% confidence intervals (CI) estimated using bootstrapping.
Results:
A scaling factor of 0.93 was used to account for ADC variation between the DWI sequence groups. The median (range) slope of the ADC time-trends was 0.05 (-0.18, 0.42) 10-3mm2/s/fraction. In 77 patients (56%), the 95% CI of the slope did not include zero.
Conclusions:
ADC changes during short course radiotherapy were detectable in 56% of the patients. Furthermore, the limited ADC variation across DWI sequences supports feasibility of multicentre investigations of MRI-linac based DWI. These findings encourage future research linking ADC to clinical outcomes in rectal cancer for potential treatment personalization.
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