{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T10:32:30Z","timestamp":1768213950741,"version":"3.49.0"},"reference-count":38,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"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":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study investigates a novel optimization framework that integrates the newly developed MCF MKM relative biological effectiveness (RBE) model into intensity modulated particle therapy (IMPT) planning for carbon ion therapy. Unlike conventional RBE models such as MKM or LEM, the MCF MKM provides detailed microdosimetric information, which improves predictive accuracy of cell survival but elevates both memory and time requirement in the optimization process. Furthermore, to the implicit nature of the MCF MKM, the gradient information, which is essential in inverse planning, is hardly accessible. In response, a randomized gradient\u2010free optimization method (RGFM) was developed to mitigate the computational challenge and achieve improved RBE dose distributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>An RBE dose optimization problem was formulated based on the discretized MCF MKM, known as the Abridged Microdosimetry Distribution Model (AMDM). The RGFM was derived to find feasible and high\u2010quality settings of monitor unit for IMPT plans without the need for explicit gradient calculations. To verify the clinical plausibility of the proposed method, it was tested on two clinical scenarios\u2014prostate and brain cases in comparison with benchmark plans derived solely from physical dose optimization. Computational times and dose\u2010volume histograms (DVHs) for both physical and RBE doses were evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RGFM algorithm successfully optimized the MCF MKM\u2010based IMPT plans. Compared to the benchmark, the RBE dose distributions showed improved coverage of the target and reduced hot spots. Such improvements were observed to coincide with trade\u2010offs in physical dose distributions. The complexity of the MCF MKM extended the computational time, yet high\u2010quality solutions were attainable in a clinically reasonable timeframe. Multi\u2010field optimization (MFO) further enhanced target coverage and critical organ sparing over single\u2010field optimization (SFO).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This proof\u2010of\u2010principle study confirmed the feasibility of using a randomized gradient\u2010free method to optimize RBE dose based on the MCF MKM in IMPT planning for carbon ion therapy. The approach provides a pathway for clinical adoption of advanced RBE models, potentially leading to more biologically informed and effective carbon ion therapy treatments. Future work involves refining algorithms, incorporating parallel computing, and performing additional case studies to improve computational efficiency and generalizability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70148","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T05:36:02Z","timestamp":1763703362000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Introducing an innovative IMPT algorithm toward clinic implementation of the MCF MKM RBE model for carbon ion therapy"],"prefix":"10.1002","volume":"52","author":[{"given":"Jindong","family":"Tong","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering University of Florida  Gainesville Florida USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongcheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering University of Florida  Gainesville Florida USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessio","family":"Parisi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic in Florida  Jacksonville Florida USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering University of Florida  Gainesville Florida USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering University of Florida  Gainesville Florida USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic in Florida  Jacksonville Florida 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