Evaluation of inter-fractional tumor target volume changes in ViewRay MRIdian LINAC adaptive radiotherapy using similarity metrics

Abstract

Tumor geometry can change during radiotherapy, and interfractional anatomical variations may compromise target coverage and dose conformity if not properly addressed. Magnetic resonance-guided adaptive radiotherapy (MRgART) enables ongoing visualization of tumor morphology and provides a framework for individualized treatment adaptation. This study quantitatively evaluated interfractional gross tumor volume (GTV) changes in patients treated with a ViewRay MRIdian LINAC system and investigated which similarity metric most reliably characterizes these variations. Retrospective MR images from 37 patients were analyzed and grouped by anatomical region into the pelvis, abdomen, and thorax. Baseline GTV (GTV) was compared with GTVs from five consecutive fractions (GTV₁-GTV₅). Geometric agreement was assessed using four similarity metrics: Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), Tanimoto similarity coefficient (TSC), and Ochiai similarity coefficient (OSC). Interfractional stability was evaluated using mean similarity values and standard deviations across fractions. The abdominal region exhibited the greatest interfractional variability, with marked volume changes observed particularly in pancreatic cancer patients. Pelvic and thoracic tumors demonstrated relatively greater geometric stability, with lung tumors showing comparatively consistent agreement across fractions. Across all anatomical regions, OSC showed the highest mean similarity values and the lowest variability, indicating greater robustness than the other metrics. Statistical analysis confirmed that OSC performed significantly better than DSC, JSC, and TSC in the pelvis and abdomen (p<0.05), while showing comparable behavior to DSC in the thorax. These findings indicate that OSC is a reliable metric for monitoring interfractional tumor geometry in MR-guided adaptive radiotherapy and may support more precise and efficient adaptive treatment strategies.

Keywords

Magnetic resonance imaging-guided linear accelerator / Adaptive radiotherapy / Gross target volume / Similarity coefficients / Magnetic resonance imaging-guided radiotherapy

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