Patients with large, locally advanced cervical cancers (LACC) are challenging to treat. The purpose of this work is to use 18F-FDG PET as planning basis for a short-course simultaneous integrated boost (SIB) in external beam radiotherapy of LACC in order to increase tumour shrinkage and likelihood of local control.
Ten previously treated patients with LACC were included, all with pre-treatment FDG PET/CT images available. The FDG avid tumour volume, MTV50, was dose escalated in silico by intensity modulated radiotherapy from the standard 1.8 Gy to 2.8 Gy per fraction for the 10 first fractions; a short-course SIB. For the 18 remaining external fractions, standard pelvic treatment followed to total PTV and MTV50 doses of 50.4 Gy and 60.4 Gy, respectively. Photon and proton treatment were considered using volumetric modulated arc treatment (VMAT) and intensity-modulated proton therapy (IMPT), respectively. All treatment plans were generated using the Eclipse Treatment Planning System (TPS). The impact of tumour shrinkage on doses to organs at risk (OARs) was simulated in the TPS for the SIB plans.
Dose escalation could be implemented using both VMAT and IMPT, with a D98 ≥ 95 % for MTV50 being achieved in all cases. The sum of the 10 fraction short-course SIB and subsequent 18 standard fractions was compared to the standard non-SIB approach by dose volume histogram (DVH) analysis. Only marginal increase of dose to OARs was found for both modalities and a small further increase estimated from tumour shrinkage. Most DVH parameters showed a mean difference below 2 %. IMPT had, compared to VMAT, reduced OAR doses in the low to intermediate dose range, but showed no additional advantage in dose escalation.
Planning of dose escalation based on a FDG avid boost volume was here demonstrated feasible. The concept may allow time for enhanced tumour shrinkage before brachytherapy. Thus, this strategy may prove clinically valuable, in particular for patients with large tumours.
This item's license is: Attribution 4.0 International