{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T12:15:06Z","timestamp":1789992906294,"version":"4.0.1"},"reference-count":35,"publisher":"IOP Publishing","issue":"21","license":[{"start":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T00:00:00Z","timestamp":1540857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T00:00:00Z","timestamp":1540857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Elekta"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2018,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The purpose of this work is to evaluate and quantify the potential radiobiological advantages of tumor tracking using the MR-linac for three disease sites: liver, pancreas and kidney.<\/jats:p>\n                  <jats:p>\n                    From each disease site, three patients were selected and 4DCT data sets were used. We applied two planning methods using the Monaco treatment planning system (\n                    <jats:italic>Elekta AB,Stockholm,Sweden<\/jats:italic>\n                    ): (1) the conventional ITV method using a 6MV Agility beam and (2) a simulated tracking method using MLC GTV tracking with a 7MV MR-linac beam model incorporating a 1.5 T transverse magnetic field. A 5\u2009mm isotropic PTV margin was added to the ITV or the GTV, and 95% of the PTV volume received 100% of the prescription dose. To evaluate the potential radiobiological advantages of tumor tracking, the normal tissue complication probabilities (NTCPs) were calculated for each organ at risk (OAR) using the Layman Kutcher Burman (LKB) model.\n                  <\/jats:p>\n                  <jats:p>The average reduction in the target volume, due to tracking, was 31.1%, 26.3% and 26.9% for liver, pancreas and kidney patients, respectively. For each OAR, the % differences in NTCP between the two methods were calculated. The mean 2 Gy equivalent OAR dose for all patients was less than 29.1 Gy, below which the NTCP for most OARs was not sensitive to equivalent uniform dose (EUD). As a result, a NTCP benefit, due to tracking, was observed in 26% of the data. For all three disease sites, the maximum NTCP improvements were for the normal kidney, the bowels, and the duodenum, with reductions in associated toxicities of 79% (radiation nephropathy), 69% (stricture\/fistula) and 25% (ulceration), respectively.<\/jats:p>\n                  <jats:p>This study demonstrates the potential benefit of using a MR-linac tracking system to reduce NTCPs. The normal kidney, the bowels and the duodenum showed the largest NTCP improvements. This, in part, is due to the rapid changes in NTCP for small EUD changes.<\/jats:p>","DOI":"10.1088\/1361-6560\/aae7fd","type":"journal-article","created":{"date-parts":[[2018,10,12]],"date-time":"2018-10-12T18:15:29Z","timestamp":1539368129000},"page":"215022","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["The radiobiological impact of motion tracking of liver, pancreas and kidney SBRT tumors in a MR-linac"],"prefix":"10.1088","volume":"63","author":[{"given":"Shahad","family":"Al-Ward","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matt","family":"Wronski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0509-7537","authenticated-orcid":false,"given":"Syed Bilal","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sten","family":"Myrehaug","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arjun","family":"Sahgal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian M","family":"Keller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2018,10,30]]},"reference":[{"key":"pmbaae7fdbib001","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1118\/1.4939808","type":"journal-article","article-title":"Evaluation of a commercial MRI Linac based Monte Carlo dose calculation algorithm with geant 4","volume":"43","author":"Ahmad","year":"2016","journal-title":"Med. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2018-04-25","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2018-10-12","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2018-10-30","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}