{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T04:09:55Z","timestamp":1778213395217,"version":"3.51.4"},"reference-count":42,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100007210","name":"Varian Medical Systems","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007210","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogeneity and patient history, which have been hypothesized to mitigate the dose delivery effectiveness when treated with radiation therapy. Providing an accurate predictive model to identify patients at risk would enable tailored follow\u2010up strategies during\u00a0treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Our goal is to demonstrate the added prognostic value of including tumor displacement amplitude in a predictive model that combines clinical features and computed tomography (CT) radiomics for 2\u2010year recurrence and survival in non\u2010small\u2010cell lung cancer (NSCLC) patients treated with curative\u2010intent stereotactic body radiation\u00a0therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cohort of 381 patients treated for primary lung cancer with radiotherapy was collected, each including a planning CT with a dosimetry plan, 4D\u2010CT, and clinical information. From this cohort, 101 patients (26.5%) experienced cancer progression (locoregional\/distant metastasis) or death within 2 years of the end of treatment. Imaging data was analyzed for radiomics features from the tumor segmented image, as well as tumor motion amplitude measured on 4D\u2010CT. A random forest (RF) model was developed to predict the overall outcomes, which was compared to three other approaches \u2014 logistic regression, support vector machine, and convolutional neural\u00a0networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A 6\u2010fold cross\u2010validation study yielded an area under the receiver operating characteristic curve of 72% for progression\u2010free survival when combining clinical data with radiomics features and tumor motion using a RF model (72% sensitivity and 81% specificity). The combined model showed significant improvement compared to standard clinical data. Model performances for loco\u2010regional recurrence and overall survival sub\u2010outcomes were established at 73% and 70%, respectively. No comparative methods reached statistical significance in any data\u00a0configuration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Combined tumor respiratory motion and radiomics features from planning CT showed promising predictive value for 2\u2010year tumor control and survival, indicating the potential need for improving motion management strategies in future studies using machine learning\u2010based prognosis\u00a0models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17586","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T08:39:05Z","timestamp":1734683945000},"page":"1926-1940","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free\u2010breathing 4D\u2010CT"],"prefix":"10.1002","volume":"52","author":[{"given":"Emilie","family":"Ouraou","sequence":"first","affiliation":[{"name":"Computer and Software Engineering Department Polytechnique Montr\u00e9al Montr\u00e9al Quebec Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marion","family":"Tonneau","sequence":"additional","affiliation":[{"name":"Radiation Oncology Department Centre hospitalier de l'Universit\u00e9 de Montr\u00e9al (CHUM) Montr\u00e9al Quebec Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William T.","family":"Le","sequence":"additional","affiliation":[{"name":"Computer and Software Engineering Department Polytechnique Montr\u00e9al Montr\u00e9al Quebec Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edith","family":"Filion","sequence":"additional","affiliation":[{"name":"Radiation Oncology Department Centre hospitalier de l'Universit\u00e9 de Montr\u00e9al (CHUM) 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