{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:51:21Z","timestamp":1789739481401,"version":"4.0.1"},"reference-count":26,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Manual intensity\u2010modulated radiotherapy (IMRT) planning for locally advanced non\u2010small cell lung cancer (LA\u2010NSCLC) is labor\u2010intensive and time\u2010consuming. Knowledge\u2010based planning (e.g., RapidPlan) improves consistency but commonly falls short in fully meeting clinical objectives in LA\u2010NSCLC cases, requiring iterative manual adjustments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and validate PlanAct, an Eclipse Scripting API (ESAPI)\u2010based module for optimizing automated IMRT planning in LA\u2010NSCLC, and to compare its performance against clinical and RapidPlan\u2010generated plans across a retrospective patient cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats: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\u2010NSCLC cases using a standardized nine\u2010beam geometry. Plans were normalized to ensure 95% planning target volume (PTV) coverage. The PlanAct\u2010optimized plans were evaluated against RapidPlan\u2010generated plans and clinically approved plans using institutional plan quality metrics, including dose\u2010volume 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      PlanAct\u2010optimized plans demonstrated significant improvement in plan quality compared to RapidPlan, with fewer unmet clinical requirements and better organ\u2010at\u2010risk sparing, particularly for the lungs (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001 for V\n                      <jats:sub>20<\/jats:sub>\n                      and D\n                      <jats:sub>mean<\/jats:sub>\n                      ). Only one PlanAct\u2010optimized plan failed to meet all dose constraints (in this case, lungs D\n                      <jats:sub>mean<\/jats:sub>\n                      ) 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>PlanAct is an effective tool to optimize automated IMRT planning in LA\u2010NSCLC. 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\u2010specific radiotherapy treatment planning systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70304","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:36:22Z","timestamp":1760013382000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["PlanAct: An eclipse scripting API\u2010based module embedding clinical optimization strategies for automated planning in locally advanced non\u2010small cell lung cancer"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3359-4514","authenticated-orcid":false,"given":"Hao","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York 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Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Chao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4288-6503","authenticated-orcid":false,"given":"Jiahan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School 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