Patient-specific PTV margins for liver stereotactic body radiation therapy determined using support vector classification with an early-warning system for margin adaptation.

PURPOSE An adaptive PTV margin strategy incorporating a volumetric tracking error assessment after each fraction is proposed for robotic stereotactic body radiation therapy (SBRT) liver treatments. METHODS AND MATERIALS A supervised machine learning algorithm employing retrospective data, which emulates a dry-run session prior to planning, is used to investigate if motion-tracking errors are less than 2 mm, and consequently, planning target volume (PTV) margins can be reduced. A fraction of data collected during the beginning of a treatment course emulates a dry-run session (mock) before planning. Twenty features are calculated using mock data and used for support vector classification (SVC). A treatment course is labeled as Class 1 if the maximum root-mean-square radial tracking error for all remaining fractions is below 2 mm, or Class 2 otherwise. We evaluate the classification using 5-fold cross-validation, leave-one-out cross-validation, 500-repeated random sub-sampling cross-validation, and the receiver operating characteristic (ROC) metric. The classification is independently cross-validated on a cohort of 48 treatment plans for other anatomical sites. A per fraction assessment of volumetric tracking errors is performed for the standard 5 mm PTV margin (PTVstd ) for courses predicted as Class 2; or for a margin reduced by 2 mm (PTVstd-2mm ) for those predicted as Class 1. We perturb the gross tumor volume (GTV) by the tracking errors for each x-ray image acquisition and calculate the fractional GTV voxel occupancy probability (Pi ) inside the PTV for each treatment fraction i. For treatment courses classified as Class 1, an early-warning system flags treatment courses having any Pi 0.99 for all patients. CONCLUSIONS SVC is proposed for the classification of different motion-tracking errors for patient courses based on a mock session before planning for SBRT liver treatments. It is feasible to implement patient-specific PTV margins in the clinic, assisted with an early-warning system to flag treatment courses that require replanning using larger PTV margins in an adaptive treatment strategy.