{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:15:07Z","timestamp":1778822107159,"version":"3.51.4"},"reference-count":51,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2018,10,17]],"date-time":"2018-10-17T00:00:00Z","timestamp":1539734400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Current radiation therapy planning uses a set of defined dose\u2013volume constraints to ensure a specified level of tumor coverage while constraining the dose distribution in the organs at risk. Such constraints are aggregated, population\u2010based quantities that do not adequately consider patient\u2010specific risk factors. Furthermore, these constraints are calculated for each organ independently and it is therefore not guaranteed that the optimal trade\u2010off between organs is achieved. We introduce a novel radiotherapy planning approach where a patient\u2010specific all\u2010cause mortality risk is minimized using inverse plan optimization. As illustration of concept, our outcome risk model incorporates patient age, sex, cardiac risk factor (<jats:styled-content style=\"fixed-case\">CRF<\/jats:styled-content>), and smoking.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and materials<\/jats:title><jats:p>We retrospectively analyzed a left\u2010sided breast cancer case and a Hodgkin's lymphoma case, both clinically treated with three\u2010dimensional conformal radiotherapy (3D\u2010<jats:styled-content style=\"fixed-case\">CRT<\/jats:styled-content>). Our objective function for inverse plan optimization was an equally weighted summation of risk models for cancer recurrence and mortality from radiation\u2010induced coronary heart disease and secondary lung and breast cancers incorporating patient age, sex, CRF, and smoking. We allowed the optimization algorithm to choose from a large set of gantry angles. The optimization task was to choose beams and optimize monitor units (<jats:styled-content style=\"fixed-case\">MU<\/jats:styled-content>s) so that overall survival was maximized (and the total risk of cancer recurrence and mortality from radiation\u2010induced causes were minimized). The sensitivity analysis was performed in the lymphoma case by changing the tumor control probability model from using mean dose (Model 1) to using generalized equivalent uniform dose (Model 2).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For the breast case in this study, the 3D\u2010<jats:styled-content style=\"fixed-case\">CRT<\/jats:styled-content> clinical plan used eight beams while the proposed 3D\u2010<jats:styled-content style=\"fixed-case\">CRT<\/jats:styled-content> outcome\u2010optimized plan used five beams, reducing the total risk \u2014 summation of the risks of recurrence and secondary disease mortality \u2014 from 3% to 2%. The mean doses to clinical target volume (<jats:styled-content style=\"fixed-case\">CTV<\/jats:styled-content>) and internal mammary nodes (<jats:styled-content style=\"fixed-case\">IMN<\/jats:styled-content>) were increased in the outcome\u2010optimized plan by 1.9 and 1.8\u00a0Gy, respectively. For the Hodgkin's lymphoma case, the clinical 3D\u2010<jats:styled-content style=\"fixed-case\">CRT<\/jats:styled-content> plan used two beams, while the proposed 3D\u2010<jats:styled-content style=\"fixed-case\">CRT<\/jats:styled-content> outcome\u2010optimized plan used three beams, reducing the total risk by 6% (from 16% to 10%). Using either of the two tumor control models for the lymphoma case resulted in outcome\u2010optimized plans where tumor control was compensated at the cost of saving organs at risk. However, the impact of sensitivity to models was comparatively large. Using Model 1 resulted in a reduction in mean target dose by 15.2 vs 7.1\u00a0Gy for Model 2. In all cases, the chosen beams in outcome\u2010optimized plans were different from clinically used beams.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed optimization strategy, supplanting dosimetric objectives with comprehensive individual risk estimates, has the potential to yield improved outcomes in terms of reduced mortality risk in cancer patients treated with radiotherapy. The approach is, however, currently limited by gaps in knowledge about the effect of compromising dose to part of the target, for example, in order to spare cardiac structures.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13211","type":"journal-article","created":{"date-parts":[[2018,9,24]],"date-time":"2018-09-24T06:00:06Z","timestamp":1537768806000},"page":"5332-5342","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Individualized estimates of overall survival in radiation therapy plan optimization \u2014 A concept study"],"prefix":"10.1002","volume":"45","author":[{"given":"Arezoo","family":"Modiri","sequence":"first","affiliation":[{"name":"School of Medicine University of Maryland Baltimore MD USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Line Bjerregaard","family":"Stick","sequence":"additional","affiliation":[{"name":"Rigshospitalet University of Copenhagen Copenhagen 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