{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T03:01:31Z","timestamp":1778641291312,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The National Key Research and Development program of China","award":["2017YFC0113904"],"award-info":[{"award-number":["2017YFC0113904"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Radiomics features can be positioned to monitor changes throughout treatment. In this study, we evaluated machine learning for predicting tumor response by analyzing CT images of lung cancer patients treated with radiotherapy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Experimental Design<\/jats:title>\n                <jats:p>For this retrospective study, screening or standard diagnostic CT images were collected for 100 patients (mean age, 67 years; range, 55\u201382 years; 64 men [mean age, 68 years; range, 55\u201382 years] and 36 women [mean age, 65 years; range, 60\u201372 years]) from two institutions between 2013 and 2017. Radiomics analysis was available for each patient. Features were pruned to train machine learning classifiers with 50 patients, then trained in the test dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Result<\/jats:title>\n                <jats:p>A support vector machine classifier with 2 radiomic features (flatness and coefficient of variation) achieved an area under the receiver operating characteristic curve (AUC) of 0.91 on the test set.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The 2 radiomic features, flatness, and coefficient of variation, from the volume of interest of lung tumor, can be the biomarkers for predicting tumor response at CT.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10278-020-00385-3","type":"journal-article","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T20:02:51Z","timestamp":1602014571000},"page":"1401-1403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy"],"prefix":"10.1007","volume":"33","author":[{"given":"Mengmeng","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,6]]},"reference":[{"issue":"4","key":"385_CR1","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/s41416-018-0201-z","volume":"119","author":"MK Thompson","year":"2018","unstructured":"Thompson MK, Poortmans P, Chalmers AJ, et al: Practice-changing radiation therapy trials for the treatment of cancer: where are we 150 years after the birth of Marie Curie?. 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