{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T18:13:48Z","timestamp":1788372828571,"version":"build-2803163510"},"reference-count":30,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T00:00:00Z","timestamp":1755993600000},"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":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Knowledge\u2010based planning (KBP) is a data\u2010driven approach that utilizes the knowledge from previous high\u2010quality treatment plans to predict dose\u2010volume histogram (DVH) parameters for organs\u2010at\u2010risk (OARs) in new cases. Research has demonstrated that KBP enhances plan quality, minimizes inter\u2010patient and inter\u2010institution variability, and significantly boosts time efficiency. However, current state\u2010of\u2010the\u2010art KBP approaches only generate one set of planning goals for one point on the Pareto optimal surface without considering potential planning trade\u2010offs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The objective of this study is to develop a KBP trade\u2010off prediction model that can effectively assist clinical decision\u2010making during the treatment planning process for patients with locally advanced non\u2010small cell lung cancer (NSCLC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We created 13 volumetric\u2010modulated arc therapy (VMAT) plan variations for each patient in our dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a053), consisting of one balanced plan and 12 alternative plans with trade\u2010off considerations. These trade\u2010off plans incorporated three levels (0\u20132) of sparing priority for each OAR, including the esophagus, lungs, heart, and spinal cord. The first three principal components (PCs) of each OAR\u2010specific DVH were used as target variables, while 26 anatomical features served as predictors. The patients were randomly divided into a training set (80%) and a test set (20%). A forward feature selection process identified the top five anatomical features, which were then used to train a random forest multi\u2010output regression model to predict the first three DVH PCs for each of the 13 plan\u2010OAR variations. We compared the performance of our trade\u2010off prediction model with that of a balanced model trained on the balanced plan without any trade\u2010off considerations. The evaluation metrics included root\u2010mean\u2010square error (RMSE) and mean absolute error (MAE) for key dose\u2010volume metrics of the DVH curves.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The trade\u2010off prediction model significantly outperformed the balanced model in terms of average RMSE (5.32 vs. 27.3) compared to the planned DVHs for all 13 treatment plans. The trade\u2010off model also achieved a lower MAE for all the clinical dose\u2010volume metrics, including spinal cord D\n                      <jats:sub>max<\/jats:sub>\n                      (12.5 vs. 15.5\u00a0Gy,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01), esophagus D\n                      <jats:sub>max<\/jats:sub>\n                      (1.7 vs. 2.7\u00a0Gy,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01), left lung V\n                      <jats:sub>20Gy<\/jats:sub>\n                      (7.8% vs. 27.5%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01), right lung V\n                      <jats:sub>20Gy<\/jats:sub>\n                      (7.4% vs. 27.8%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01), and heart V\n                      <jats:sub>30Gy<\/jats:sub>\n                      (10.8% vs. 20.9%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our proposed KBP trade\u2010off model reliably predicts plan tradeoff variations for the treatment of NSCLC patients. Incorporating this model into the pre\u2010planning process may serve as a decision support tool for physicians and planners by providing feasible trade\u2010off estimations, thereby improving the efficiency of the treatment planning workflow.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18068","type":"journal-article","created":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T14:55:14Z","timestamp":1756047314000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Knowledge\u2010based trade\u2010off prediction for NSCLC treatment planning using multi\u2010output regression"],"prefix":"10.1002","volume":"52","author":[{"given":"Tenzin","family":"Kunkyab","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology The Mount Sinai Hospital  New York New York 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