Magnetic Resonance-guided Radiation Therapy (MRgRT) allows online adaptations (OA) of the treatment plan to optimize daily dose distribution based on patient's anatomy, just before fraction delivery. The aim of this study is to evaluate feasibility and the dosimetric improvement of the OA workflow implemented in our institution for locally advanced pancreatic cancer (LAPC) patients, in terms of target coverage and organs at risk (OARs) sparing. We retrospectively analysed 8 LAPC patients treated with MRgRT in combination with the OA approach, using video-assisted inspiratory breath-hold for a total of 38 fractions with a dose ranging from 30 Gy to 40 Gy in 5 fractions. Dose distribution of the baseline plan was first calculated based on daily anatomy, obtaining a “predicted” plan to assess the dosimetric improvement. If the dose distribution did not meet the constraints set in the planning phase, PTV, GTV and OARs were re-contoured within a distance of 3 cm from the PTV external edge and a new online “adaptive” plan was generated. Other clinical and planning parameters were also evaluated to assess the feasibility and the dosimetic benefit of the online adaptive workflow. Out of 38 total fractions, 26 (68.4%) were adapted online and 12 (31.6%) were delivered using the baseline plan. The use of the adaptive workflow resulted to be feasible in our clinical practice and advantageous in all the patients: mean PTV V95% increased by 10.8% (5.7–20.8) while mean CTV V98% of 12.6% (7.3–17.7). Also OARs V33 and V25 showed a positive trend avoiding unnecessary irradiation. OA workflow improves the dosimetric benefit of MRgRT, preventing the occurrence of high-doses to OARs and increasing the safety of stereotactic treatment for LAPC, without any drawback for our daily clinical practice routine.

Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.

The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61–0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.