Hao Guo, Tenzin Kunkyab, Yang Lei, Kenneth Rosenzweig, Robert Samstein, Ming Chao, Tian Liu, Junyi Xia, Jiahan Zhang
{"title":"PlanAct：一个基于eclipse脚本api的模块，嵌入临床优化策略，用于局部晚期非小细胞肺癌的自动化规划。","authors":"Hao Guo, Tenzin Kunkyab, Yang Lei, Kenneth Rosenzweig, Robert Samstein, Ming Chao, Tian Liu, Junyi Xia, Jiahan Zhang","doi":"10.1002/acm2.70304","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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 (<i>p</i> < 0.001 for V<sub>20</sub> and D<sub>mean</sub>). Only one PlanAct-optimized plan failed to meet all dose constraints (in this case, lungs D<sub>mean</sub>) 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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 10","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70304","citationCount":"0","resultStr":"{\"title\":\"PlanAct: An eclipse scripting API-based module embedding clinical optimization strategies for automated planning in locally advanced non-small cell lung cancer\",\"authors\":\"Hao Guo, Tenzin Kunkyab, Yang Lei, Kenneth Rosenzweig, Robert Samstein, Ming Chao, Tian Liu, Junyi Xia, Jiahan Zhang\",\"doi\":\"10.1002/acm2.70304\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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 (<i>p</i> < 0.001 for V<sub>20</sub> and D<sub>mean</sub>). Only one PlanAct-optimized plan failed to meet all dose constraints (in this case, lungs D<sub>mean</sub>) due to a large PTV volume, compared to 18 RapidPlan and 10 clinical plans. 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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.
期刊介绍：
Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission.
JACMP will publish:
-Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500.
-Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed.
-Technical Notes: These should be no longer than 3000 words, including key references.
-Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents.
-Book Reviews: The editorial office solicits Book Reviews.
-Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics.
-Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
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