Integrating Chemotherapy, Radiotherapy and MGMT Status with Deep-Learning Cellular Tumor Volumetry Sharpens Prediction of Glioblastoma Recurrence on Post-operative MRI
Clicks: 1
ID: 320559
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #79 of 103 articles by views in Neuro-Oncology Advances
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Background Distinguishing glioblastoma recurrence from post-treatment effects on magnetic resonance imaging (MRI) remains a major diagnostic challenge. While recent models consider MGMT status, most machine learning models addressing this problem assume uniform chemotherapy and radiotherapy exposure, overlooking real-world treatment variability. This study aimed to determine whether integrating chemotherapy, radiotherapy, and MGMT status together with automated cellular tumor volume improves predictive accuracy in post-treatment glioblastoma assessment. Methods We retrospectively analyzed 135 post-surgical MRI examinations from 97 glioblastoma patients treated between January 2008 and December 2022. The dataset included 105 confirmed recurrences and 30 cases of treatment-related change. A total of 8,465 radiomic features were extracted from five MRI sequences (T1-weighted pre/post-contrast, T2-weighted, FLAIR, and Restricted Spectrum Imaging [RSI] cellularity maps). Cellular tumor volumes were automatically segmented using nnU-Net and combined with chemotherapy radiotherapy and MGMT status. Models were trained using Extremely Randomized Trees with nested Monte Carlo cross-validation. Results The baseline Top10 radiomic model achieved an area under the curve (AUC) of 0.765. Incorporation of nnU-Net–derived cellular tumor volume (VolCT) significantly improved performance to 0.809 (p = 2.84 × 10-7). Adding chemotherapy, radiotherapy, or MGMT status to the Top10+VolCT model yielded further gains, with AUCs ranging from 0.828 to 0.830 (all p ≤ 0.032). The full model combining VolCT with chemotherapy, radiotherapy, and MGMT achieved the best performance, with an AUC of 0.853 (p = 0.0006 vs Top10+VolCT). Conclusions Incorporating chemotherapy, radiotherapy and MGMT status improves posttreatment glioblastoma classification. Deep learning–derived cellular tumor volumetry further enhances radiomics-based performance, highlighting the value of combining clinical context with advanced computational imaging.
| Reference Key |
openalex_W7167913066
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Taha Belbadaoui, Andrew Forester, Farzad Khalvati, Louis Gagnon |
| Journal | Neuro-Oncology Advances |
| Year | 2026 |
| DOI |
10.1093/noajnl/vdag178
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.