Tam, Lydia;
Han, Michelle;
Wright, Jason;
Toescu, Sebastien;
Campion, Andrew;
Shpanskaya, Katie;
Mankad, Kshitij;
... Yeom, Kristen; + view all
(2020)
IMG-10. MRI-BASED RADIOMIC PROGNOSTIC MARKERS OF DIFFUSE MIDLINE GLIOMA.
Presented at: 19th International Symposium on Pediatric Neuro-Oncology (ISPNO 2020), Karuizawa, Japan.
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Abstract
Background: Diffuse midline gliomas (DMG) are lethal pediatric brain tumors with dismal prognoses. Presently, MRI is the mainstay of disease diagnosis and surveillance. We aimed to identify prognostic image-based radiomics markers of DMG and compare its performance to clinical variables at presentation. / / Methods: 104 treatment-naïve DMG MRIs from five centers were used (median age = 6.5 yrs; 18 males, median OS = 11 mos). We isolated tumor volumes of T1-post-contrast (T1gad) and T2-weighted (T2) MRI for PyRadiomics high-dimensional feature extraction. 900 features were extracted on each image, including first-order statistics, 2D/3D shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Neighboring Gray Tone Difference Matrix, and Gray Level Dependence Matrix, as defined by Imaging Biomarker Standardization Initiative. Overall survival (OS) served as outcome. 10-fold cross-validation of LASSO Cox regression was used to predict OS. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Concordance metric was used to assess the Cox model. / / Results: Nine radiomic features were selected from T1gad (2 texture wavelet) and T2 (5 first-order features [1 original, 4 wavelet], 2 texture features [1 wavelet, 1 log-sigma]). This model demonstrated significantly higher performance than a clinical model alone (C: 0.68 vs 0.59, p < 0.001). Adding clinical features to radiomic features slightly improved prediction, but was not significant (C = 0.70, p = 0.06). / / Conclusion: Our pilot study shows a potential role for MRI-based radiomics and machine learning for DMG risk stratification and as image-based biomarkers for clinical therapy trials.
| Type: | Conference item (Presentation) |
|---|---|
| Title: | IMG-10. MRI-BASED RADIOMIC PROGNOSTIC MARKERS OF DIFFUSE MIDLINE GLIOMA |
| Event: | 19th International Symposium on Pediatric Neuro-Oncology (ISPNO 2020) |
| Location: | Karuizawa, Japan |
| Dates: | December 13 - 16, 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://doi.org/10.1093/neuonc/noaa222.346 |
| Language: | English |
| Additional information: | © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
| Keywords: | magnetic resonance imaging, biological markers, foreign medical graduates, glioma, signs and symptoms, diagnosis, diagnostic imaging, patient prognosis, brain tumor, childhood, surveillance, medical, prognostic markers, stratification, Cox proportional hazards models, tumor volume, transverse spin relaxation time, machine learning, radiomics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10214182 |
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