Can the Day 0 CT-scan predict the post-implant scanning? Results from 136 prostate cancer patients. (August 2017) - Cattani, Federica
Highlights: The impact of timing of the post-implant CT scan on the dosimetric results. 136 prostate cancer patients received monotherapy with 125 I brachytherapy implants. Dosimetric quality parameters of the Day 50 were higher than those of the Day 0 scan. Linear functions calculate D90 and V100 values at Day 50 based on the Day 0 findings. Predicting the Day 50 dosimetry from the Day 0 scan is a possible alternative for a Day 50 scan. Abstract: Purpose: Post-implant CT-scanning is an essential part of permanent prostate brachytherapy. However, the evaluation of post-implant CT dosimetry is not straightforward due to the edema that can modify the dose to the prostate and to the organs at risk. The aim of this study is to evaluate the impact of the timing of the post-implant CT-scan on the dosimetric results and to verify if the Day 0 scan findings can predict Day 50 scanning. Methods: 136 consecutive patients who received monotherapy with I-125 implants were selected for this study. Two sets of 8 dosimetric quality parameters corresponding to 2 different CT-scans (Day 0 and Day 50) were calculated and compared. The dosimetric parameters included are the percentage volume of the post-implant prostate receiving 80%, 100% and 150% of the prescribed dose, the doses covering 80% and 90% of the prostate volume and the Dose Homogeneity Index. The values of the dose covering 1 cm 3 of the rectum and urethra were assessed. Results: All the dosimetric parameters of the Day 50 were higher than those of the Day 0 scan. Linear functions were obtained that calculate D90 and V100 values at Day 50 based on the Day 0 findings. Rectal and urethral parameters tended to be underestimated on Day 0 CT-scan relative to Day 50 based dosimetry. Conclusions: Predicting the Day 50 dosimetry from the Day 0 scan could be a possible alternative to a Day 50 scan only in specific situations, but with a degree of uncertainty in the predicted values.