{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:14:38Z","timestamp":1792793678315,"version":"4.5.0"},"reference-count":24,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"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":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Measurement\u2010based patient specific quality assurance (PSQA) is an increasingly debated topic among medical physicists. Developments like online adaptive radiotherapy and same\u2010day stereotactic treatments limit the time to do measurement\u2010based PSQA. Herein, we develop a predictive machine learning model to supplement PSQA by predicting the gamma passing rate (GPR) per stereotactic arc. This streamlines PSQA, providing planners the insight to replan potentially sub\u2010optimal plans, to mitigate machine time inefficiencies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>122 patients that had previously received HyperArc stereotactic radiosurgery\/radiotherapy on a TrueBeam LINAC (Millenium 120 MLCs, 6MV\u2010FFF) were used to generate a long short\u2010term memory (LSTM) recurrent neural network to predict the GPR for a 2%\/2\u00a0mm criteria. GPRs were discretized into three classes: Ideal (\u226595%), Investigate [85%\u201395%), and Replan\u00a0(&lt;85%). In total, 468 VMAT arcs were used for this model with a class distribution of 370 (Ideal), 65 (Investigate), and 33 (Replan). To counteract the imbalanced data, the minority classes were over\u2010sampled using synthetic minority over\u2010sampling technique to generate a balanced dataset. The LSTM model was trained in Python with an 80\u201020 training\u2010testing stratified split. Individual class sensitivity and specificity were recorded following a one versus all method. The final model was deployed clinically through Eclipse Scripting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model demonstrated the following (sensitivity, specificity) for the testing data: Ideal (78.4%, 87.2%), Investigate (75.7%, 89.9%), and Replan (93.2%, 96.6%). The primary focus of this model is to identify failing beams and allow the planner to address this prior to running the PSQA, as such the Replan class was the most important for evaluation. A sensitivity of 93.2% indicates that the model will identify 93.2% of HyperArc plans that need to be replanned with a very high certainty due to the 96.6% specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The predictive GPR model developed within this research enables HyperArc planners to immediately assess the GPR for each stereotactic arc and preemptively replan potentially failing arcs to optimize the PSQA machine time.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70225","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T01:39:13Z","timestamp":1755826753000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A predictive quality assurance model for patient\u2010specific gamma passing rate of hyperarc\u2010based stereotactic radiotherapy and radiosurgery of brain metastases"],"prefix":"10.1002","volume":"26","author":[{"given":"Shane","family":"McCarthy","sequence":"first","affiliation":[{"name":"Medical Physics Graduate Program, Department of Radiation Medicine University of Kentucky  Lexington Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brent","family":"Harrison","sequence":"additional","affiliation":[{"name":"College of Engineering University of Kentucky  Lexington Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damodar","family":"Pokhrel","sequence":"additional","affiliation":[{"name":"Medical Physics Graduate Program, Department of Radiation Medicine University of Kentucky  Lexington Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12810"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2015.08.010"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4722967"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2003.10.014"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16536"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13876"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14175"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13858"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2020.577620"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15393"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.07.049"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13622"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2024.103208"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13375"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1285555"},{"key":"e_1_2_10_17_1","unstructured":"ChawlaNV BowyerKW HallLO KegelmeyerWP SMOTE: synthetic minority over\u2010sampling technique. 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