{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T20:53:40Z","timestamp":1768683220129,"version":"3.49.0"},"reference-count":49,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Current radiation therapy (RT) treatment planning relies mainly on pre\u2010defined dose\u2010based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population\u2010based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient\u2010specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity\u2010modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient\u2010specific dose response models into the planning process. In this strategy, we integrate the concept of utility\u2010based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non\u2010small cell lung cancer (NSCLC) patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and materials<\/jats:title><jats:p>We developed a prioritized approach to patient\u2010specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet\u2010dose contributions to regions\u2010of\u2010interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade\u2010off based on individualized dose\u2010response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose\u2010based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3\u20135 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some patients, the utility\u2010based plans resulted in similar local control estimates with decreased estimated toxicity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed optimization approach, where the maximization of a patient's RT plan utility is prioritized over the minimization of standardized dose metrics, has the potential to improve treatment outcomes by directly accounting for variability within a patient population. The implementation of the utility\u2010based objective function offers an intuitive, humanized approach to biological optimization in which planning trade\u2010offs are explicitly optimized.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15940","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T14:43:48Z","timestamp":1661179428000},"page":"6279-6292","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Direct incorporation of patient\u2010specific efficacy and toxicity estimates in radiation therapy plan optimization"],"prefix":"10.1002","volume":"49","author":[{"given":"Daniel F","family":"Polan","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marina A","family":"Epelman","sequence":"additional","affiliation":[{"name":"Department of Industrial and Operations Engineering University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor W","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Industrial and Operations Engineering University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilun","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"},{"name":"Department of Biostatistics University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Varsta","sequence":"additional","affiliation":[{"name":"Varian Medical Systems  Palo Alto California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel R","family":"Owen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Jarema","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles K","family":"Matrosic","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shruti","family":"Jolly","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caitlin A","family":"Schonewolf","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew 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