{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:02:11Z","timestamp":1789700531228,"version":"4.0.1"},"reference-count":27,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"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":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to evaluate the feasibility of fast Fourier transform (FFT) based spectral features for predicting volumetric modulated arc therapy (VMAT) gamma passing rates (GPRs) in virtual patient\u2010specific quality assurance (PSQA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 481 VMAT treatment plans were retrospectively collected. Multileaf collimator (MLC) trajectories and monitor units (MU) were extracted from control points (CPs) and interpolated onto a uniform gantry\u2010angle grid. FFT\u2010based power spectra were then computed to extract global, spatial\u2010axis, and angular\u2010axis spectral features characterizing modulation complexity. These spectral features, alongside 25 conventional plan complexity metrics, were used to train Random Forest (RF) and XGBoost models for GPR regression and PSQA pass\/fail classification. We employed a nested cross\u2010validation strategy and used SHapley Additive exPlanations (SHAP) for model interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Spectral features and conventional plan complexity metrics correlated significantly with GPRs, particularly spatial\u2010axis high\u2010frequency energy ratios and total spectral energy. In the regression task, the XGBoost model utilizing the combined feature set achieved the lowest mean absolute error (MAE) of 1.095\u00a0\u00b1\u00a00.182. For classification, the RF model with combined features yielded an AUC of 0.886\u00a0\u00b1\u00a00.043, an accuracy of 0.844\u00a0\u00b1\u00a00.066, and an F1 score of 0.602\u00a0\u00b1\u00a00.094. SHAP analysis confirmed that spectral energy, spatial\u2010axis low\u2010 and high\u2010frequency energy ratios, were major contributors to predicting PSQA failures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>FFT\u2010based spectral analysis provides physically interpretable features for characterizing VMAT modulation complexity. These features showed feasible performance for GPR prediction and PSQA classification. These findings support their potential utility as interpretable inputs for virtual VMAT PSQA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70772","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T04:29:06Z","timestamp":1787891346000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study"],"prefix":"10.1002","volume":"27","author":[{"given":"Bing","family":"Yan","sequence":"first","affiliation":[{"name":"School of Instrument Science and Optoelectronics Engineering Hefei University of Technology  Hefei Anhui China"},{"name":"Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine University of Science and Technology of China  Hefei Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Optoelectronics Engineering Hefei University of Technology  Hefei Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College Huazhong University of Science and Technology  Wuhan Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chichao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Optoelectronics Engineering Hefei University of Technology  Hefei Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aidong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine University of Science and Technology of China  Hefei Anhui 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