Pre-treatment QA for online-adaptive PT: Status-quo workflow assessment and sanity check development

Purpose/Objective
Within a dedicated consortium, two clinical proton therapy (PT) centers collaborate with two industrial partners (PT system and treatment planning provider, respectively) to jointly develop, implement and evaluate an industrial solution for online-adaptive proton therapy (OAPT). This also requires a dedicated approach for patient-specific QA (pre- and post-treatment PSQA). As first step, we conducted a comprehensive assessment of the current clinical pre-treatment PSQA protocols in the two clinical PT facilities. Furthermore, so-called “sanity checks” have been defined, which play a crucial role in the OAPT QA concept: First, they speed up and automate checks that are currently performed manually and second, in combination with secondary dose calculation, they provide additional trust/evidence that the treatment is safe and as expected, before the delivery is started.

Materials/Methods
The current PSQA workflows (from plan approval until delivery of first fraction, Fig. 1A) were systematically analyzed for both PT facilities. Center 1 employs the MOSAIQ (Elekta AB, SE) oncology information system (OIS) in combination with RayStation (RaySearch Laboratories, Stockholm, SE) as treatment planning system (TPS). Center 2 utilizes the RayCare OIS (RaySearch Laboratories, SE) in combination with RayStation. The two workflows are hereinafter referred to as “Separate-OIS” and “Integrated-OIS”, respectively. Time required for all human operations in these systems, including data preparation for phantom measurements and double checks was assessed via the number of required mouse clicks and manual entries.
Sanity checks (SCs) are automated QA routines, aiming to replace manual checks carried out by the medical physics expert (MPE). They serve as an additional safety mechanism, which guarantees the plausibility of plan parameter adjustments induced by the adaptation process and verifies the integrity of transferred DICOM data. To achieve an OAPT-ready PSQA workflow while providing at least the same level of risk mitigation as the current protocol, we defined SCs for adapted treatment plans, based on existent QA procedures. We incorporated these features in a conceptual PSQA-workflow for OAPT (Fig. 1B), in which SCs are complemented by secondary dose calculation and log file-based QA to enable phantom-less PSQA. Furthermore, manual sub-processes in the current PSQA protocol, which are directly covered by SCs in the OAPT workflow were identified.

Results
The status-quo pre-treatment PSQA workload for the Separate-OIS workflow was estimated to 402 clicks, distributed over four chronological sub-processes: 40% plan approval and -documentation, 11% phantom-QA preparation, 19% 1st MPE check and 30% 2nd MPE check (Fig. 2A). For the Integrated-OIS workflow, a total of 269 clicks were recorded with 18%, 5%, 34% and 43% for the same sub-processes, respectively (Fig. 2B). The Separate-OIS and Integrated-OIS workflows demand 18 vs. 10 manual entries of treatment parameters, respectively. The lower number of clicks required for the Integrated-OIS workflow are associated with the seamless integration of OIS and TPS, combined with less time spent on the manual creation and review of report documents exchanged by separate systems.
Twenty-six SCs were defined, divided in 13 exact (“hard”) checks and 13 tolerance threshold-based (“soft”) checks. They are situated at two subsequent, designated workflow points: (1) After plan approval in the TPS and (2) after DICOM data transfer from the OIS to the treatment control system (TCS). SCs operate on patient data, prescription, gantry- and couch parameters as well as image-/structure sets, beam parameters and dose calculation settings (Fig. 2C/D). From the total 402 clicks recorded for PSQA operations in the Separate-OIS workflow, 47 (12%) clicks related to plan release and MPE checks would be directly covered by SCs in the envisioned OAPT workflow. For the Integrated-OIS workflow, 45 clicks (17%) would be covered by SCs. Notably, this portion only reflects the direct impact of SCs on the status-quo PSQA workload. The full OAPT-PSQA workflow will lead to a substantial additional decrease of manual operations (e.g. due to phantom-less QA and automated documentation), which was out of the scope of this work.

Conclusion
Time- and labor-intensive operations in the current PSQA workflow have been systematically assessed for two clinical PT centers. SCs were defined to automate key processes with high manual workload, enabling phantom-less PSQA in a future OAPT workflow and beyond. In addition, they would also speed-up today’s pre-treatment PSQA workflow, providing an even broader benefit. This work therefore represents a relevant step towards the implementation of OAPT for industrial PT solutions, ensuring a high level of safety while speeding up the PSQA process. In the next step, tolerance thresholds for soft SCs will be determined in a retrospective study, followed by a sensitivity analysis.