Paper Proffered Program Therapy Physics

An In-House Treatment Planning Automation System (TPAS) for Curative-Intent SABR and Rapid Process Palliative Radiotherapy: Multi-Site Clinical Validation

Abstract
Purpose

To demonstrate that TPAS can generate high-quality plans across the full spectrum of external-beam radiotherapy, from rapid palliative treatments to complex curative SABR.

Methods

TPAS was evaluated in two clinical settings at our center. (1) Curative/SABR cohort: 124 consecutive automated plans (51 SABR, 73 non-SABR) across brain metastases, CNS, liver, GYN, anal canal, and prostate sites (Jan–Mar 2024). (2) Palliative cohort: 20 consecutive CT-sim-to-treat TPAS-VMAT cases were replanned using parallel-opposed pairs (POP) on the same datasets. Dosimetric (conformity, homogeneity, OAR sparing) and workflow metrics (computation time, physician sign-off latency, labor savings) were compared.

Results

In the curative/SABR cohort, 80% of plans required no manual optimization and 100% passed QA, with median time savings of 2.7 h (SABR) and 0.7 h (non-SABR), a projected annual labor savings of CAD $105–133k. In the palliative cohort, TPAS significantly outperformed POP, achieving a higher conformity number (0.85 vs. 0.29, p < 0.001), a lower homogeneity index (8.8% vs. 12.0%, p = 0.01), Intermediate-dose spillage R50% is better for TPAS plan (2.84 ± 0.50 vs 4.79 ± 1.72, p<0.001) and reduced OAR doses for both Dmax (%) (95.48 ± 12.03 vs 105.49 ± 7.35, p =0.004) and Dmean(%) (40.13 ± 20.42 vs 46.47 ± 20.75, p= 0.03). In this setting the TPAS workflow was independent of dosimetry bottlenecks, reducing multi-day turnaround times by generating plans in under 20 minutes and enabling a median oncologist review-to-sign-off time of 15.5 minutes.

Conclusion

A single automation platform (TPAS) simultaneously delivers superior plan quality and dramatic workflow acceleration for both highly conformal curative/SABR cases and urgent same-day palliative treatments, offering a scalable model for comprehensive radiotherapy automation in large provincial programs.

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