A multi-institutional dummy run on segmentation variability and plan quality of stereotactic body radiotherapy for oligometastatic disease

针对寡转移性疾病立体定向放射治疗的分割变异性和计划质量的多机构模拟试验

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Abstract

BACKGROUND AND PURPOSE: Oligometastatic disease represents limited metastatic burden, and local ablative therapies such as stereotactic body radiotherapy (SBRT) may improve survival. However, inter-institutional variability in target segmentation and treatment planning can compromise treatment quality. This study aimed to evaluate the segmentation variability and dose distribution quality of SBRT in oligometastatic settings using a multi-institutional dummy run approach. METHODS AND MATERIALS: Sixty-nine institutions were provided with two anonymized cases of adrenal and spine metastases to delineate targets and organs at risk (OARs) and create intensity-modulated radiotherapy plans following a protocol. Variability was quantified using the Dice similarity coefficient (DSC), Hausdorff distance, and mean distance to agreement. Plan qualities were assessed using the Paddick conformity index, modified gradient index, and a new three-dimensional conformity-gradient index (3D-CGI). Knowledge-based planning (KBP) was applied to explore potential improvements in OAR sparing. RESULTS: All submitted plans met protocol dose constraints. However, substantial segmentation variability was observed, particularly for the spine case. Among 136 plans, 79% demonstrated acceptable conformity and dose gradients, with 3D-CGI < 6 correlating with favorable distributions. Mean DSC was 0.93 for the clinical target volume and 0.76 for the cauda equina, which showed the highest variability. KBP reduced OAR doses for the adrenal case but showed limited impact for the spine case. CONCLUSIONS: Although dose constraints were achieved, segmentation variability remained substantial, particularly for the cauda equina in the spine case. These findings emphasize inter-institutional differences and the need for standardization and tools to improve SBRT consistency.

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