{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T03:41:49Z","timestamp":1787629309530,"version":"build-2736575974"},"reference-count":45,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"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":[[2020,8]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation\u2010induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning\u2010based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We investigated whether convolutional neural networks (CNNs) can automatically associate abdominal computed tomography (CT) images and RT dose plans with post\u2010RT toxicities without being provided segmentation of abdominal organs. The CNNs were also applied to study RT plans, where doses at specific anatomical regions were reduced\/increased, with the aim to pinpoint critical regions sparing of which significantly reduces toxicity risks. The obtained risk maps were computed for individual anatomical regions inside the liver and statistically compared to the existing clinical studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A database of 122 liver stereotactic body RT (SBRT) executed at Stanford Hospital from July 2004 and November 2015 was assembled. All patients treated for primary liver cancer, mainly hepatocellular carcinoma and cholangiocarcinoma, with complete follow\u2010ups were extracted from the database. The SBRT treatment doses ranged from 26 to 50\u00a0Gy delivered in 1\u20135 fractions for primary liver cancer. The patients were followed up for 1\u201368\u00a0months depending on the survival time. The CNNs were trained to recognize acute and late grade 3+ biliary stricture\/obstruction, hepatic failure or decompensation, hepatobiliary infection, liver function test (LFT) elevation or\/and portal vein thrombosis, named for convenience hepatobiliary (HB) toxicities. The toxicity prediction accuracy was of 0.73 measured in terms of the area under the receiving operator characteristic curve. Significantly higher risk scores (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) of HB toxicity manifestation were associated with irradiation for the hepatobiliary tract in comparison to the risk scores for liver segments I\u2013VIII and portal vein. This observation is in strong agreement with anatomical and clinical expectations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this work, we proposed and validated a universal deep learning\u2010based solution for the identification of radiosensitive anatomical regions. Without any prior anatomical knowledge, CNNs automatically recognized the importance of hepatobiliary tract sparing during liver SBRT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.14235","type":"journal-article","created":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T07:05:03Z","timestamp":1589439903000},"page":"3721-3731","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy"],"prefix":"10.1002","volume":"47","author":[{"given":"Bulat","family":"Ibragimov","sequence":"first","affiliation":[{"name":"Department of Computer Science University of Copenhagen Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego A. S.","family":"Toesca","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel T.","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering City University of Hong Kong Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert C.","family":"Koong","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology MD Anderson Cancer Center Houston Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan R.","family":"Vogelius","sequence":"additional","affiliation":[{"name":"Department of Oncology Faulty of Health &amp; Medical Sciences Rigshospitalet University of Copenhagen Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.08.074"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.07.013"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2016.10.003"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-019-0064-5"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aac4441"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.10.020"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.02.025"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.10.024"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-07586-x"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.05.054"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.04.033"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12045"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa8d09"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13122"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.10.024"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.11.028"},{"key":"e_1_2_7_19_1","unstructured":"SimpleITK: SimpleITK(n.d.).https:\/\/itk.org\/SimpleITKDoxygen120\/html\/index.html(accessed December 11 2019)."},{"key":"e_1_2_7_20_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS SunJ.Deep Residual Learning for Image Recognition;2016:770\u2013778.http:\/\/openaccess.thecvf.com\/content_cvpr_2016\/html\/He_Deep_Residual_Learning_CVPR_2016_paper.html(accessed December 6 2019).","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"e_1_2_7_22_1","unstructured":"keras\u2010team\/keras\u2010applications GitHub. 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