eprintid: 10054586 rev_number: 50 eprint_status: archive userid: 608 dir: disk0/10/05/45/86 datestamp: 2018-08-21 13:27:10 lastmod: 2021-10-18 22:35:19 status_changed: 2019-06-24 16:33:01 type: article metadata_visibility: show creators_name: Skogen, K creators_name: Schulz, A creators_name: Helseth, E creators_name: Ganeshan, B creators_name: Dormagen, JB creators_name: Server, A title: Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis ispublished: pub divisions: UCL divisions: B02 divisions: C10 divisions: D17 divisions: FI6 keywords: Glioblastoma, brain metastases, diffusion tensor imaging, magnetic resonance imaging, peritumoral edema, texture analysis note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: BACKGROUND: Texture analysis has been done on several radiological modalities to stage, differentiate, and predict prognosis in many oncologic tumors. PURPOSE: To determine the diagnostic accuracy of discriminating glioblastoma (GBM) from single brain metastasis (MET) by assessing the heterogeneity of both the solid tumor and the peritumoral edema with magnetic resonance imaging (MRI) texture analysis (MRTA). MATERIAL AND METHODS: Preoperative MRI examinations done on a 3-T scanner of 43 patients were included: 22 GBM and 21 MET. MRTA was performed on diffusion tensor imaging (DTI) in a representative region of interest (ROI). The MRTA was assessed using a commercially available research software program (TexRAD) which applies a filtration histogram technique for characterizing tumor and peritumoral heterogeneity. The filtration step selectively filters and extracts texture features at different anatomical scales varying from 2 mm (fine) to 6 mm (coarse). Heterogeneity quantification was obtained by the statistical parameter entropy. A threshold value to differentiate GBM from MET with sensitivity and specificity was calculated by receiver operating characteristic (ROC) analysis. RESULTS: Quantifying the heterogeneity of the solid part of the tumor showed no significant difference between GBM and MET. However, the heterogeneity of the GBMs peritumoral edema was significantly higher than the edema surrounding MET, differentiating them with a sensitivity of 80% and specificity of 90%. CONCLUSION: Assessing the peritumoral heterogeneity can increase the radiological diagnostic accuracy when discriminating GBM and MET. This will facilitate the medical staging and optimize the planning for surgical resection of the tumor and postoperative management. date: 2019-03 date_type: published official_url: https://doi.org/10.1177%2F0284185118780889 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green article_type_text: Journal Article verified: verified_manual elements_id: 1562349 doi: 10.1177/0284185118780889 language_elements: English lyricists_name: Ganeshan, Balaji lyricists_id: BGANE86 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Acta Radiologica volume: 60 number: 3 pagerange: 356-366 event_location: England issn: 1600-0455 citation: Skogen, K; Schulz, A; Helseth, E; Ganeshan, B; Dormagen, JB; Server, A; (2019) Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiologica , 60 (3) pp. 356-366. 10.1177/0284185118780889 <https://doi.org/10.1177/0284185118780889>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10054586/31/Ganeshan_Texture%20analysis%20on%20diffusion%20tensor%20imaging.pdf document_url: https://discovery.ucl.ac.uk/id/eprint/10054586/3/Texture%20analysis%20Figure%201.jpg document_url: https://discovery.ucl.ac.uk/id/eprint/10054586/8/Texture%20analysis%20Figure%202.jpg document_url: https://discovery.ucl.ac.uk/id/eprint/10054586/13/Texture%20analysis%20Figure%203.jpg document_url: https://discovery.ucl.ac.uk/id/eprint/10054586/18/Texture%20analysis%20Figure%204.jpg document_url: https://discovery.ucl.ac.uk/id/eprint/10054586/23/Texture%20analysis%20Figure%205.jpg