{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:06:04Z","timestamp":1791547564604,"version":"4.3.4"},"reference-count":29,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000905","name":"Commonwealth Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000905","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Open\u2010source GPU\u2010based Monte Carlo (MC) proton dose calculation algorithms provide high speed and unparalleled accuracy but can be complex to integrate with new applications and remain slower than GPU\u2010based pencil beam (PB) methods, which sacrifice some physical accuracy for sub\u2010second plan calculation. We developed and validated a Python package implementing a GPU\u2010based double Gaussian PB algorithm for intensity\u2010modulated proton therapy (IMPT) planning research applications requiring a simple, widely compatible, and ultra\u2010fast proton dose calculation solution.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Beam parameters were derived from pristine Bragg peaks generated with MC for 98 energies in our clinical treatment planning system (TPS). We validated the PB approach against measurements by comparing lateral spot profiles (using single\u2010Gaussian sigma) and proton ranges (using R80) for pristine Bragg peaks, as well as spread\u2010out Bragg peaks (SOBPs) in water. Further comparisons of PB and MC from the TPS were performed in a heterogeneous digital phantom and patient plans for four cancer sites using 3D gamma passing rates and dose metrics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The PB algorithm enabled dose calculation following a single Python import statement. Mean\u00a0\u00b1\u00a0standard deviation (SD) errors in sigma, R80, and SOBP dose were 0.05\u00a0\u00b1\u00a00.01, 0.0\u00a0\u00b1\u00a00.1\u00a0mm, and 0.4\u00a0\u00b1\u00a01.1%, respectively. Mean\u00a0\u00b1\u00a0SD patient plan computation time was 0.28\u00a0\u00b1\u00a00.07\u00a0s for PB versus 4.68\u00a0\u00b1\u00a02.68\u00a0s for MC. Mean\u00a0\u00b1\u00a0SD gamma passing rate at 2%\/2\u00a0mm criteria was 96.0\u00a0\u00b1\u00a05.1%, and the mean\u00a0\u00b1\u00a0SD percent difference in dose metrics was 0.5\u00a0\u00b1\u00a03.6%. PB accuracy degraded beyond bone and lung boundaries, characterized by inaccuracies in lateral proton scatter.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We developed a GPU\u2010based proton PB algorithm compiled as a Python package, providing simple beam modeling, interface, and fast dose calculation for IMPT plan optimization research and development. Like other PB algorithms, accuracy is limited in highly heterogeneous regions such as the thorax.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.70093","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T23:49:36Z","timestamp":1744242576000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Python package for fast GPU\u2010based proton pencil beam dose calculation"],"prefix":"10.1002","volume":"26","author":[{"given":"Mahasweta","family":"Bhattacharya","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Calin","family":"Reamy","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University  Baltimore Maryland 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