{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:09:52Z","timestamp":1779253792429,"version":"3.51.4"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"\n Background<\/jats:title>\n Highly conformal radiotherapy techniques, such as stereotactic radiosurgery and stereotactic ablative radiotherapy, require steep dose fall-off to spare organs at risk (OARs). The dose-volume histogram (DVH) provides limited spatial information, whereas the dose gradient curve (DGC) offers quantitative assessment of dose fall-off but has seen limited clinical use due to time-consuming manual processing.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n This study aimed to develop and validate an automated DGC and DVH analysis platform to enable accurate and rapid dose gradient evaluation for radiotherapy plan assessment.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The platform automatically processes DICOM RT dose and RT structure files, with optional RT Plan input for direct prescription dose extraction, to compute differential and cumulative dose gradient index (dDGI and cDGI), DVH metrics, and clinically informed dose gradient quality grading. Computational validation was performed using AAPM TG-119 phantom datasets, including prostate, head and neck, C-shape, and multi-target cases, at 1.25\u00a0mm and 2.5\u00a0mm dose grid resolutions. Clinical validation was conducted using 142 Gamma Knife stereotactic radiosurgery plans for vestibular schwannoma from the publicly available Vestibular-Schwannoma-SEG dataset (The Cancer Imaging Archive).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The platform reduced computation time from over 2 hours (original R-based workflow) to under 10 seconds, representing &gt;99.8% efficiency improvement. All TG-119 cases met or exceeded recommended PTV coverage and OAR tolerances. Using prescription doses extracted from DICOM RT Plan file and marching-cubes-based surface area estimation, dDGI at the prescription dose ranged from 0.312 to 0.792\u00a0mm on the 1.25\u00a0mm grid and from 0.327 to 0.659\u00a0mm on the 2.5\u00a0mm grid; cDGI at 50% of the prescription dose ranged from 12.4 to 15.7\u00a0mm (1.25\u00a0mm) and from 11.7 to 15.5\u00a0mm (2.5\u00a0mm). Clinical validation with 142 vestibular schwannoma Gamma Knife SRS cases (median target volume 0.881 cc, range 0.036\u20137.183 cc; Rx = 12\u201313 Gy) yielded dDGI at Rx =\n \n \n \n 0.294<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.089<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n mm with Paddick conformity index =\n \n \n \n 0.584<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.075<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and gradient index =\n \n \n \n 2.770<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.218<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n . The dDGI was strongly correlated with target volume (Pearson\n \n \n \n r<\/mml:mi>\n =<\/mml:mo>\n 0.885<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n p<\/mml:mi>\n \u00a0<\/mml:mo>\n <<\/mml:mo>\n \u00a0<\/mml:mo>\n \n \n 10<\/mml:mn>\n <\/mml:mrow>\n \n \u2212<\/mml:mo>\n 48<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ) but independent of conformity index (\n \n \n \n r<\/mml:mi>\n =<\/mml:mo>\n 0.040<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n p<\/mml:mi>\n <\/mml:math>\n <\/jats:inline-formula>\n = 0.634), confirming that gradient steepness and conformity assess distinct plan quality aspects. Paired T1\u2013T2 MRI-based reproducibility analysis (\n \n \n \n n<\/mml:mi>\n =<\/mml:mo>\n 30<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ) demonstrated excellent agreement (dDGI\n \n \n \n r<\/mml:mi>\n =<\/mml:mo>\n 0.987<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , mean difference 3.3%).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The proposed platform enables rapid, automated DGC analysis practical for routine plan evaluation. The integrated quality grading bridges the gap between quantitative dose gradient metrics and actionable clinical decisions, complementing conventional DVH-based assessment.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fonc.2026.1826856","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:44:38Z","timestamp":1779252278000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated dose-gradient curve and dose-volume histogram analysis platform: development, validation, and clinical decision support with TG-119 datasets"],"prefix":"10.3389","volume":"16","author":[{"given":"Han-Back","family":"Shin","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Gachon University Gil Medical Center","place":["Incheon, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wonyoung","family":"Cho","sequence":"additional","affiliation":[{"name":"Oncosoft Inc.","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young Eun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Gachon University Gil Medical Center","place":["Incheon, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Sung","family":"Kim","sequence":"additional","affiliation":[{"name":"Oncosoft Inc.","place":["Seoul, Republic of Korea"]},{"name":"Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"KiHoon","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Gachon University Gil Medical Center","place":["Incheon, Republic of Korea"]},{"name":"Department of Radiation Oncology, Gachon University College of Medicine","place":["Incheon, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3438081","article-title":"Stereotactic body radiation therapy: the report of AAPM Task Group 101","volume":"37","author":"Benedict","year":"2010","journal-title":"Med Phys"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.62784","article-title":"Appropriateness of dose attenuation margin outside the gross tumor volume (GTV) in volumetric-modulated arc-based radiosurgery for brain metastasis with the steepest dose gradient outside the GTV and biologically effective dose 80 Gy to GTV boundary","volume":"16","author":"Ohtakara","year":"2024","journal-title":"Cureus"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/0360-3016(91)90168-4","article-title":"Dose-volume histograms","volume":"21","author":"Drzymala","year":"1991","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196664","article-title":"Dose gradient curve: a new tool for evaluating dose gradient","volume":"13","author":"Sung","year":"2018","journal-title":"PloS One"},{"key":"B5","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3171\/sup.2006.105.7.194","article-title":"A simple dose gradient measurement tool to complement the conformity index","volume":"105","author":"Paddick","year":"2006","journal-title":"J Neurosurg"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3171\/jns.2000.93.supplement_3.0219","article-title":"A simple scoring ratio to index the conformity of radiosurgical treatment plans. 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