ArticleJournal of applied clinical medical physics2025
PlanAct: An eclipse scripting API-based module embedding clinical optimization strategies for automated planning in locally advanced non-small cell lung cancer.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A rule-based, feedback-driven framework for fully automated prostate VMAT planning.Journal of applied clinical medical physics · 2026Article
- PlanningCopilot: An agentic framework integrating ESAPI modules for autonomous treatment planning in lung radiotherapy.Medical physics · 2026Article
- Improving portability of knowledge-based planning using an LLM-driven plan refinement framework in lung radiotherapy.Medical physics · 2026Article
- Article
- PlanAct: An eclipse scripting API-based module embedding clinical optimization strategies for automated planning in locally advanced non-small cell lung cancer.Journal of applied clinical medical physics · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundManual 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.
methodsPlanAct 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.
resultsPlanAct-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 V
conclusionsPlanAct 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.
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