{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:11:38Z","timestamp":1790392298526,"version":"4.1.0"},"reference-count":53,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":25,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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":["Medical Physics"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate and efficient dose calculation is essential for online adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results for pencil beam scanning proton therapy (PBSPT) in recent years, but existing DL\u2010based dose prediction methods still suffer from limited generalizability and an inability to effectively handle outlier clinical cases. This may lead to inaccurate dose delivery to targets or excessive irradiation to organs at risk (OARs), thereby compromising the safety and efficacy of online adaptive proton therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To design a physics\u2010aware and generalizable AI\u2010based PBSPT dose prediction method that incorporates underlying physics to enhance generalizability, particularly in handling outlier clinical cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzed PBSPT plans of 103 prostate (93 for training and 10 for testing) and 78 lung cancer patients (68 for training and 10 for testing) from our institution, with each case comprising CT images and structure sets. Using the doses generated by our Monte Carlo\u2010based dose engine as the reference standard, we compared three methods: the region of interest (ROI)\u2010based method, the beam mask and sliding window method, and the proposed noisy probing dose method, which rapidly generates a low\u2010statistics dose via uniformly weighted spots on an expanded spot\u2010placement target volume without optimization. To evaluate the generalizability of these methods to rare treatment planning scenarios, 12 cases with uncommon beam angles or prescription doses were used to assess their performance, which was evaluated using dose\u2010volume histogram (DVH) indices, 3D Gamma passing rates (3%\/2\u00a0mm\/10%), and Dice coefficients for dose agreement, while prediction times were measured to gauge model efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed noisy probing dose method consistently outperformed the ROI\u2010based and beam mask baselines across all evaluated metrics, with more accurate dose agreement and superior generalizability. For DVH indices, the noisy probing dose method achieved the smallest deviation in clinical target volume (CTV) dose coverage: in prostate cancer, CTV D98 deviation was reduced by 45% (from 0.53\u00a0\u00b1\u00a00.22\u00a0Gy [RBE] for ROI\u2010based) and 29% (from 0.41\u00a0\u00b1\u00a00.28\u00a0Gy [RBE] for beam mask) to 0.29\u00a0\u00b1\u00a00.06\u00a0Gy [RBE]; in lung cancer, similar improvements were observed, with CTV D98 deviations reduced to 0.34\u00a0\u00b1\u00a00.12\u00a0Gy [RBE]. The 3D Gamma passing rates improved to 99.65%\u00a0\u00b1\u00a01.15% for prostate targets and 97.04%\u00a0\u00b1\u00a01.17% for lung targets. The dice coefficients of the 90% iso\u2010dose lines were also the highest with the noisy probing dose method (prostate: 0.983\u00a0\u00b1\u00a00.005; lung: 0.967\u00a0\u00b1\u00a00.01). For the 12 outlier cases, the noisy probing dose method maintained superior generalizability, yielding higher 3D Gamma passing rates (prostate targets: 96.79%\u00a0\u00b1\u00a00.83%, OARs: 94.29%\u00a0\u00b1\u00a01.01%; lung targets: 93.38%\u00a0\u00b1\u00a01.34%, OARs: 93.95%\u00a0\u00b1\u00a01.32%), demonstrating robust generalizability to rare clinical scenarios. The dose predictions for all testing cases were completed within 0.3\u00a0seconds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A novel noisy probing dose method was proposed for PBSPT dose prediction in prostate and lung cancer patients. By embedding more proton\u2010specific physics, this method demonstrated an improvement in the generalizability of dose prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70509","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T03:58:13Z","timestamp":1779854293000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: Physics enhances generalizability"],"prefix":"10.1002","volume":"53","author":[{"given":"Lian","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason M.","family":"Holmes","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona 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