Abstract
Definitive radiotherapy (RT) has been shown to be a successful method of treating prostate cancer (PCa) patients. Through radiomics, a quantitative analysis of medical images, it is possible to adapt treatment early on, which may prevent or mitigate future adverse events. During RT of PCa, low-field magnetic resonance (MR) images, taken with a LINAC onboard imaging system in a process known as magnetic resonance-guided radiotherapy (MRgRT), are used to improve treatment accuracy via superior setup compared to x-ray methods. This work investigated baseline repeatability of radiomic features (RFs) by comparing planning MR images (pMR) with first-fraction setup images (FX1) taken with onboard MRI. The changes in RFs following RT were also looked at with the use of last-fraction setup images (FX5). Earlier research has investigated the use of planning images from cone beam CT (CBCT), but to our knowledge no research has previously shown the relationship with onboard MRI. The correlation between FX1 images and 3T diagnostic MR (dT2) images was also studied. Forty-three first and second order radiomic features extracted from these images were compared by calculating Lin’s concordance correlation coefficient (with Benjamini-Hochberg correction for multiple comparisons) between the modalities. FX1 and pMR images were correlated (p<0.05) for all but one RF. 12 RFs correlated between pMR and dT2 images. There was a noticeable change in correlation values for RFs when looking at FX1 and FX5 images, with only 15 correlating significantly. The change in correlation values between pMR and FX5 images was comparable to that between FX1 and FX5 images, with 33 features having a CCC value deviation of less than 0.1. These results demonstrate that RF features are repeatable across different images of the same modality without treatment intervention. This study has also shown a noticeable, reproducible change in RFs as RT goes on. Reproducibility of RFs between different modalities was not strong. This study demonstrated that we can reliably use onboard MRI to observe day-to-day feature changes as a result of RT.