PlanAct: An eclipse scripting API-based module embedding clinical optimization strategies for automated planning in locally advanced non-small cell lung cancer.

BACKGROUND

Manual intensity-modulated radiotherapy (IMRT) planning for locally advanced non-small cell lung cancer (LA-NSCLC) is labor-intensive and time-consuming. Knowledge-based planning (e.g., RapidPlan) improves consistency but commonly falls short in fully meeting clinical objectives in LA-NSCLC cases, requiring iterative manual adjustments.

PURPOSE: To develop and validate PlanAct, an Eclipse Scripting API (ESAPI)-based module for optimizing automated IMRT planning in LA-NSCLC, and to compare its performance against clinical and RapidPlan-generated plans across a retrospective patient cohort.

METHODS

PlanAct was developed with modular functions to automate key tasks in IMRT plan generation and optimization. PlanAct was manually executed on 56 anonymized retrospective LA-NSCLC cases using a standardized nine-beam geometry. Plans were normalized to ensure 95% planning target volume (PTV) coverage. The PlanAct-optimized plans were evaluated against RapidPlan-generated plans and clinically approved plans using institutional plan quality metrics, including dose-volume constraints for the esophagus, spinal cord, lungs, heart, larynx, and PTV. Statistical comparisons were performed to assess differences in plan quality and unmet dosimetric requirements.

RESULTS

PlanAct-optimized plans demonstrated significant improvement in plan quality compared to RapidPlan, with fewer unmet clinical requirements and better organ-at-risk sparing, particularly for the lungs (p < 0.001 for V20 and Dmean). Only one PlanAct-optimized plan failed to meet all dose constraints (in this case, lungs Dmean) due to a large PTV volume, compared to 18 RapidPlan and 10 clinical plans. Even in anatomically challenging cases, PlanAct produced more favorable dose distributions, with superior hotspot control.

CONCLUSIONS

PlanAct is an effective tool to optimize automated IMRT planning in LA-NSCLC. It produced plans comparable to or better than clinical plans, even in challenging cases. Its modular architecture makes it promising for integration into future fully autonomous, patient-specific radiotherapy treatment planning systems.