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This study aimed to determine whether FTB maps help in distinguishing tumor progression (TP) from treatment-related abnormalities (TRA) in post-treatment glioblastoma patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      Unenhanced and contrast-enhanced T1-weighted and perfusion-weighted sequences of patients with new contrast-enhancing lesions were retrospectively included. Semiautomatic segmentation of these lesions was performed. Using predefined relative cerebral blood volume (rCBV) thresholds, voxels within this segmentation were classified as FTB\n                      <jats:sub>low<\/jats:sub>\n                      , FTB\n                      <jats:sub>mid<\/jats:sub>\n                      , or FTB\n                      <jats:sub>high.<\/jats:sub>\n                      Patient outcome was determined by clinical and radiological follow-up. Non-parametric statistics were used to compare the FTB quantification. Diagnostic accuracy was evaluated with the area under the receiver operating characteristic curve (AUROC) and Youden\u2019s J. The difference between AUROCs was tested using bootstrapping.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Fifty-nine patients were included, 35 of them showing TP (59%). The percentages of voxels classified as FTB\n                      <jats:sub>low<\/jats:sub>\n                      and FTB\n                      <jats:sub>high<\/jats:sub>\n                      were significantly different between the groups (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.031 and\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.010, respectively). Using the percentage of voxels classified as FTB\n                      <jats:sub>high<\/jats:sub>\n                      as a cutoff to differentiate TP from TRA yielded an AUROC of 0.70 (95% confidence interval: 0.56\u20120.84), while FTB\n                      <jats:sub>low<\/jats:sub>\n                      yielded 0.67 (0.52\u20130.82), without a significant difference (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.466). The highest sensitivity and specificity based on the cutoff of 24% of voxels classified as FTB\n                      <jats:sub>high<\/jats:sub>\n                      coverage, were 63% and 79%, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>FTB quantification yielded fair accuracy in the early detection of glioblastoma TP. Future research is needed to investigate how to use FTB maps in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>Early discrimination between TP and TRA, even with fair accuracy, can help in alleviating some uncertainty in glioblastoma patients. A clear visualization of lesion heterogeneity provided by FTB-maps could allow for more targeted treatment options and targeted follow-up.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Follow-up of patients with glioblastoma is complicated by the similar appearance of treatment effects and tumor growth on MRI.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Perfusion imaging provides a basis for the creation of FTB maps. These visualize the heterogeneity of brain lesions.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Quantitative analysis of FTB maps can help differentiate tumor growth from treatment effect with reasonable accuracy.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00685-3","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T14:03:22Z","timestamp":1772460202000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantification of fractional tumor burden for the early detection of post-treatment glioblastoma progression"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4791-5681","authenticated-orcid":false,"given":"Siem","family":"Herings","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"de Wit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baris","family":"Saglik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manoj","family":"Mannil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rik","family":"van den Elshout","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Arens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anja","family":"van der Kolk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Scheenen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dylan","family":"Henssen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"685_CR1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.3322\/caac.21613","volume":"70","author":"AC Tan","year":"2020","unstructured":"Tan AC, Ashley DM, Lopez GY, Malinzak M, Friedman HS, Khasraw M (2020) Management of glioblastoma: state of the art and future directions. 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Usage of Large Language Models or other generative AI. The authors declare that no large language models or other forms of generative AI were used in the writing of this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}