PURPOSE: CBCT-based online adaptive radiation therapy is carried out using a synthetic CT, sCT, created through deformable registration between the patient-specific fan-beam CT, FBCT, and daily CBCT. Ethos 2.0 allows for plan calculation directly on HyperSight CBCT and uses AI-informed tools for daily contouring without the use of a priori information. This breaks an important link between daily adaptive sessions and initial reference plan preparation. This work explores adaptive radiation therapy for spine metastases without prior patient-specific imaging or treatment planning. We hypothesize that adaptive plans can be created when patient-specific positioning and anatomy is incorporated only once the patient has arrived at the treatment unit. METHODS AND MATERIALS: An Ethos 2.0 emulator was used to create initial reference plans on ten patient-specific FBCTs. Reference plans were also created using FBCTs of i) a library patient with clinically acceptable contours and ii) a water-equivalent phantom with placeholder contours. Adaptive sessions were simulated for each patient using the three different starting points. Resulting adaptive plans were compared to determine the significance of patient-specific information prior to the start of treatment. RESULTS: The library patient and phantom reference plans did not generate adaptive plans that differed significantly from the standard workflow for all clinical constraints for target coverage and organ at risk sparing (p>
0.2). Gamma comparison between the three adaptive plans for each patient (3%/3 mm) demonstrated overall similarity of dose distributions (pass rate >
95%), for all but two cases. Failures occurred mainly in low-dose regions, highlighting difference in fluence used to achieve the same clinical goals. CONCLUSIONS: This study confirmed feasibility of a procedure for treatment of spine metastases that does not rely on previously acquired patient-specific imaging, contours or plan. Reference-free direct-to-treatment workflows are possible and can condense a multi-step process to a single location with dedicated resources.