Resolving spatial response heterogeneity in glioblastoma.
Purpose: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Methods: Based on automated tumor segmentation and longitudina...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 51; no. 12; pp. 3685 - 3696 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180037093&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180037093 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Oct2024 vid: 51 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 180037093 177671199 10.1007/s00259-024-06782-y 180037093 ppf: 3685 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Resolving spatial response heterogeneity in glioblastoma. aug: au: Ziegenfeuter, Julian Delbridge, Claire Bernhardt, Denise Gempt, Jens Schmidt-Graf, Friederike Hedderich, Dennis Griessmair, Michael Thomas, Marie Meyer, Hanno S Zimmer, Claus Meyer, Bernhard Combs, Stephanie E Yakushev, Igor Metz, Marie-Christin Wiestler, Benedikt affil: Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany sug: ab: Purpose: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Methods: Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Results: Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Conclusion: Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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