ABSTRACT
Objective: To quantitatively characterize the dosimetric effects of long on-couch time in prostate cancer patients treated with adaptive ultra-hypofractionated radiotherapy (UHF-RT) on 1.5-Tesla magnetic resonance (MR)-linac. Materials and methods: Seventeen patients consecutively treated with UHF-RT on a 1.5-T MR-linac were recruited. A 36.25 Gy dose in five fractions was delivered every other day with a boost of 40 Gy to the whole prostate. We collected data for the following stages: pre-MR, position verification-MR (PV-MR) in the Adapt-To-Shape (ATS) workflow, and 3D-MR during the beam-on phase (Bn-MR) and at the end of RT (post-MR). The target and organ-at-risk contours in the PV-MR, Bn-MR, and post-MR stages were projected from the pre-MR data by deformable image registration and manually adapted by the physician, followed by dose recalculation for the ATS plan. Results: Overall, 290 MR scans were collected (85 pre-MR, 85 PV-MR, 49 Bn-MR and 71 post-MR scans). With a median on-couch time of 49 minutes, the mean planning target volume (PTV)-V95% of all scans was 97.83 ± 0.13%. The corresponding mean clinical target volume (CTV)-V100% was 99.93 ± 0.30%, 99.32 ± 1.20%, 98.59 ± 1.84%, and 98.69 ± 1.85%. With excellent prostate-V100% dose coverage, the main reason for lower CTV-V100% was slight underdosing of seminal vesicles (SVs). The median V29 Gy change in the rectal wall was -1% (-20%-17%). The V29 Gy of the rectal wall increased by >15% was observed in one scan. A slight increase in the high dose of bladder wall was noted due to gradual bladder growth during the workflow. Conclusions: This 3D-MR-based dosimetry analysis demonstrated clinically acceptable estimated dose coverage of target volumes during the beam-on period with adaptive ATS workflow on 1.5-T MR-linac, albeit with a relatively long on-couch time. The 3-mm CTV-PTV margin was adequate for prostate irradiation but occasionally insufficient for SVs. More attention should be paid to restricting high-dose RT to the rectal wall when optimizing the ATS plan.
ABSTRACT
BACKGROUND: The selection of adaptive strategies in MR-guided adaptive radiotherapy (MRgART) usually relies on subjective review of anatomical changes. However, this kind of review may lead to improper selection of adaptive strategy for some fractions. PURPOSE: The purpose of this study was to develop prediction models based on deformation vector field (DVF) features for automatic and accurate strategy selection, using prostate cancer as an example. METHODS: 100 fractions of 20 prostate cancer patients were retrospectively selected in this study. Treatment plans using both adapt to position (ATP) strategy and adapt to shape (ATS) strategy were generated. Optimal adaptive strategy was determined according to dosimetric evaluation. DVFs of the deformable image registration (DIR) of daily MRI and CT simulation scans were extracted. The shape, first order statistics, and spatial features were extracted from the DVFs, subjected to further selection using the minimum redundancy maximum relevance (mRMR) method. The number of features (Fn ) was hyper-tuned using bootstrapping method, and then Fn indicating a peak area under the curve (AUC) value was used to construct three prediction models. RESULTS: According to subjective review, the ATS strategy was adopted for all 100 fractions. However, the evaluation results showed that the ATP strategy could have met the clinical requirements for 23 (23%) fractions. The three prediction models showed high prediction performance, with the best performing model achieving an AUC value of 0.896, corresponding accuracy (ACC), sensitivity (SEN) and specificity (SPC) of 0.9, 0.958, and 0.667, respectively. The features used to construct prediction models included four features extracted from y direction of DVF (DVFy ) and mask, one feature from z direction of DVF (DVFz ). It indicated that the deformation along the anterior-posterior direction had a greater impact on determining the adaptive strategy than other directions. CONCLUSIONS: DVF-feature-based models could accurately predict the adaptive strategy and avoid unnecessary selection of time-consuming ATS strategy, which consequently improves the efficiency of the MRgART process.
