Tadic, Tony

Abstract Background MR‐guided adaptive radiotherapy (ART) allows for daily plan optimization based on patient‐specific anatomy. Accumulated doses, driven by deformable image registration (DIR), of daily fractions can provide cumulative dose metrics and insights into toxicity and tumor control. In prostate ART, inter‐ and intra‐factional deformations, particularly due to bladder and rectum, pose a challenge to accurate DIR generation. Purpose To quantify geometric and dosimetric accuracy of a proposed prostate MR‐to‐MR DIR approach to support MR‐guided ART dose accumulation. Methods We evaluated DIR accuracy in 25 patients treated with 30 Gy in five fractions on a 1.5 T MR‐linac using an adaptive workflow. For all patients, a reference MR was used for planning, with three images collected at each fraction: adapt MR for adaptive planning, verify MR for pretreatment position verification and beam‐on for capturing anatomy during radiation delivery. We assessed three DIR approaches: intensity‐based, intensity‐based with controlling structures (CS), and intensity‐based with controlling structures and points of interest (CS + P). DIRs were performed between the reference and fraction images and within fractions (adapt‐to‐verify and adapt‐to‐beam‐on). For the evaluation, we propagated CTV, bladder, and rectum contours using the DIRs and compared each to manually delineated contours using Dice similarity coefficient, mean distance to agreement, and dose–volume metrics. Results CS and CS + P improved geometric agreement between manual and propagated contours over intensity‐only DIR. For example, mean distance to agreement (DTA mean ) for reference‐to‐beam‐on intensity‐only DIR was 0.131 ± 0.009 cm (CTV), 0.46 ± 0.08 cm (bladder), and 0.154 ± 0.013 cm (rectum). For the CS, the DTA mean values were 0.018 ± 0.002, 0.388 ± 0.14, and 0.036 ± 0.013 cm. Finally, for CS + P, these values were 0.015 ± 0.001, 0.025 ± 0.004, and 0.021 ± 0.002 cm. Dosimetrically, comparing CS and CS + P for reference to beam‐on DIRs resulted in a change of CTV D98% from [−29 cGy, 19 cGy] to [−18 cGy, 26 cGy], bladder D5cc from [−51 cGy, 544 cGy] to [−79 cGy, 36 cGy], and rectum D1cc from [−106 cGy, 72 cGy] to [−52 cGy, 74 cGy]. Conclusion CS improved geometric and dosimetric accuracy over intensity‐only DIR, with CS + P providing further performance improvement, particularly for bladder. However, session image segmentation remains a challenge, which may be addressed with automated contouring.

Abstract BACKGROUND Post-operative prognostication in glioma patients remains a clinical challenge, particularly given the heterogeneity in tumor biology and patient outcomes. We evaluated whether image-derived features from post-operative imaging improve survival modeling beyond established clinicopathologic factors. METHODS We analyzed a cohort of 71 post-surgical glioma patients treated at Princess Margaret Cancer Centre with known clinicopathologic features including age, histological classification, IDH mutation status, MGMT methylation status, extent of resection, and performance status. A baseline Cox proportional hazards model was fit to clinical variables. Radiomic features were extracted using PyRadiomics from post-operative CT and MRI across three regions of interest: whole brain, tumor bed, and peritumoral ring. Features underwent stepwise unsupervised reduction (variance filtering, exclusion of volume-correlated features, and removal of remaining collinear features), followed by dimensionality reduction via principal component analysis. Combined clinical and imaging models were evaluated in the overall cohort and stratified by IDH mutation status. RESULTS In the overall cohort, the clinical model was highly prognostic (C-index 0.74, p ~ 3.2×10⁻⁶). Adding imaging features improved performance (C-index 0.79) but was not significant (p ~ 0.31). Subset analysis revealed differential effects by IDH status. In IDH-mutant gliomas (n=24), neither clinical nor combined models were significant. In IDH wild-type glioblastoma (n=47), the clinical model achieved C-index 0.75 (p=0.04). The addition of imaging features significantly improved model performance (C-index 0.83, p=0.03), with selected imaging components contributing independent prognostic value after adjustment for clinical variables. Post-hoc analysis of radiomic feature importance (random forest ranking) revealed that top contributing features were drawn from both CT and MRI modalities, with most sourced from the whole brain region. CONCLUSIONS In post-surgical patients with IDH wild-type glioblastoma, image-derived features from post-operative imaging can significantly enhance prognostic modeling beyond known clinicopathologic factors. These preliminary findings support the potential role of radiomic biomarkers in refining risk stratification for this patient subgroup.