<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>14</volume><submitter>Macarie AC</submitter><pubmed_abstract>Glioblastoma multiforme (GBM), the most aggressive primary brain tumour, exhibits low survival rates due to its rapid growth, infiltrates surrounding brain tissue, and is highly resistant to treatment. One major challenge is oedema infiltration, a fluid build-up that provides a path for cancer cells to invade other areas. MRI resolution is insufficient to detect these infiltrating cells, leading to relapses despite chemotherapy and radiotherapy. In this work, we propose a new multiscale mathematical modelling method, to explore the oedema infiltration and predict tumour relapses. To address tumour relapses, we investigated several possible scenarios for the distribution of remaining GBM cells within the oedema after surgery. Furthermore, in this computational modelling investigation on tum</pubmed_abstract><journal>Frontiers in oncology</journal><pagination>1447010</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11669604</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Post-operative glioblastoma cancer cell distribution in the peritumoural oedema.</pubmed_title><pmcid>PMC11669604</pmcid><pubmed_authors>Macarie AC</pubmed_authors><pubmed_authors>Okasha M</pubmed_authors><pubmed_authors>Hossain-Ibrahim K</pubmed_authors><pubmed_authors>Steele JD</pubmed_authors><pubmed_authors>Suveges S</pubmed_authors><pubmed_authors>Trucu D</pubmed_authors></additional><is_claimable>false</is_claimable><name>Post-operative glioblastoma cancer cell distribution in the peritumoural oedema.</name><description>Glioblastoma multiforme (GBM), the most aggressive primary brain tumour, exhibits low survival rates due to its rapid growth, infiltrates surrounding brain tissue, and is highly resistant to treatment. One major challenge is oedema infiltration, a fluid build-up that provides a path for cancer cells to invade other areas. MRI resolution is insufficient to detect these infiltrating cells, leading to relapses despite chemotherapy and radiotherapy. In this work, we propose a new multiscale mathematical modelling method, to explore the oedema infiltration and predict tumour relapses. To address tumour relapses, we investigated several possible scenarios for the distribution of remaining GBM cells within the oedema after surgery. Furthermore, in this computational modelling investigation on tum</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024</publication><modification>2025-04-18T22:01:40.609Z</modification><creation>2025-04-07T09:57:46.35Z</creation></dates><accession>S-EPMC11669604</accession><cross_references><pubmed>39726706</pubmed><doi>10.3389/fonc.2024.1447010</doi></cross_references></HashMap>