{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:04:52Z","timestamp":1784217892105,"version":"3.55.0"},"reference-count":10,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Running VMAT plan\u2010complexity surveillance directly inside an oncology information system (OIS) makes it practical to monitor plan quality across a large, multi\u2010linac program. Complexity metrics are increasingly used to anticipate patient\u2010specific QA (PSQA) outcomes, but few studies span multiple years or tie complexity monitoring to the OIS at scale, which leaves published benchmarks hard to generalize and prospective monitoring slow to enter routine practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To describe an automated Python pipeline integrated with the MOSAIQ OIS for computing eight established VMAT plan complexity metrics from DICOM\u2010RT files at scale, and to characterize the resulting distributions across a three\u2010year institutional VMAT program stratified by treatment planning system (TPS), anatomic site, dosimetrist, and calendar year, with correlation to matched PSQA outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>All clinically approved VMAT plans from March 2023 to March 2026 were identified via MOSAIQ OIS (v2.6, Elekta AB) at a multisite comprehensive cancer center. A purpose\u2010built Python tool queried the MOSAIQ SQL Server database and parsed DICOM\u2010RT plan files to compute MU Factor, mean aperture area (MAA), mean leaf gap (MLG), aperture irregularity (AI), small aperture scores (SASs) at 5 and 10\u00a0mm, modulation complexity score for VMAT (MCSv), and Modulation Index Total (MITotal). Plans were stratified by TPS (Pinnacle3 vs. Monaco), anatomic site group (11 groups), dosimetrist, and year. Mann\u2013Whitney U and Kruskal\u2013Wallis tests were applied for group comparisons. PSQA pass rates (3%\/2\u00a0mm global gamma, \u226595% threshold) were extracted from MOSAIQ and matched to complexity data for 3402 of 3751 plans (90.7%); Spearman rank correlations quantified complexity\u2013pass\u2010rate associations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The institutional median (IQR) MU Factor was 2.96 (2.26\u20134.00) MU\/cGy and MCSv was 0.966 (0.955\u20130.978). Monaco plans showed significantly higher MU Factors (+32%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001), narrower apertures (MLG \u221216%), and higher MCSv (+1.0%) than Pinnacle3 plans. Pelvis and head and neck plans had the highest median MU Factors (3.77 and 3.40 MU\/cGy); Breast plans were most efficient (2.10 MU\/cGy). Institutional MU Factor rose 20% over three years. Of 3402 PSQA\u2010matched plans, the mean gamma pass rate was 98.7% (median 99.6%); 115 (3.4%) fell below 95%. All complexity metrics correlated significantly with pass rate (all\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001); AI (\u03c1\u00a0=\u00a0\u22120.358) and MCSv (\u03c1\u00a0=\u00a0+0.336) were the strongest predictors. Monaco plans achieved a higher mean PSQA pass rate than Pinnacle3 (99.0%\u00a0vs. 98.5\u201398.6%) despite greater modulation. Significant linac\u2010level variation in pass rates was observed (97.3%\u201399.2%), not fully explained by plan complexity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>OIS\u2010integrated VMAT complexity and PSQA surveillance is feasible at scale and yields clinically useful information. AI and MCSv were the strongest complexity\u2010based predictors of PSQA outcome. The TPS\u2010, site\u2010, and linac\u2010specific complexity profiles we observed argue for risk\u2010stratified PSQA design. The pipeline is readily transferable to other MOSAIQ\u2010based programs for prospective plan\u2010quality monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70691","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T04:56:02Z","timestamp":1783400162000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010TPS institutional surveillance of VMAT plan complexity and PSQA results over three years"],"prefix":"10.1002","volume":"27","author":[{"given":"Sotiri","family":"Stathakis","sequence":"first","affiliation":[{"name":"Department of Medical Physics and Radiation Oncology Mary Bird Perkins Cancer Center  Baton Rouge Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ara","family":"Alexandrian","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Radiation Oncology Mary Bird Perkins Cancer Center  Baton Rouge Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mason","family":"Heath","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Radiation Oncology Mary Bird Perkins Cancer Center  Baton Rouge Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2818738"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3240488"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.05.060"},{"issue":"11","key":"e_1_2_12_5_1","first-page":"232","article-title":"Quantified VMAT plan complexity in relation to measurement\u2010based quality assurance results","volume":"21","author":"Younge KC","year":"2020","journal-title":"J Appl Clin Med Phys"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4810969"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/23\/7315"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190270"},{"issue":"1065","key":"e_1_2_12_9_1","article-title":"Complexity metrics in a multi\u2010institutional dosimetry audit","volume":"89","author":"Hussein M","year":"2016","journal-title":"Br J Radiol"},{"issue":"2","key":"e_1_2_12_10_1","first-page":"270","article-title":"Comparison of TPS for prostate VMAT","volume":"104","author":"Perrotti F","year":"2012","journal-title":"Radiother Oncol"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12810"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70691","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/acm2.70691","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70691","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:15:42Z","timestamp":1784214942000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.70691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":10,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["10.1002\/acm2.70691"],"URL":"https:\/\/doi.org\/10.1002\/acm2.70691","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"2026-04-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70691"}}