Enhancing AI-based decision support system with automatic brain tumor segmentation for EGFR mutation classification

Gökmen İnan, Neslihan; Kocadağlı, Ozan; Cevik, Serdar; Aktan, Cagdas; Eghbali, Reza; Liu, Chunlei

doi:10.1007/s11517-025-03447-2

Enhancing AI-based decision support system with automatic brain tumor segmentation for EGFR mutation classification

Gökmen İnan N., Kocadağlı O., Cevik S., Aktan C., Eghbali R., Liu C.

Medical and Biological Engineering and Computing, cilt.64, sa.1, ss.197-217, 2026 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 64 Sayı: 1
Basım Tarihi: 2026
Doi Numarası: 10.1007/s11517-025-03447-2
Dergi Adı: Medical and Biological Engineering and Computing
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, BIOSIS, CINAHL, Compendex, EMBASE, INSPEC, MEDLINE, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Health Research Premium Collection (ProQuest), Pharma Collection (ProQuest), Technology Collection (ProQuest)
Sayfa Sayıları: ss.197-217
Anahtar Kelimeler: Brain tumours, Glioblastoma, Deep learning, Automatic segmentation, EGFR mutation
Sağlık Bilimleri Üniversitesi Adresli: Hayır

Özet

Glioblastoma (GBM) carries poor prognosis; epidermal-growth-factor-receptor (EGFR) mutations further shorten survival. We propose a fully automated MRI-based decision-support system (DSS) that segments GBM and classifies EGFR status, reducing reliance on invasive biopsy. The segmentation module (UNet SI) fuses multiresolution, entropy-ranked shearlet features with CNN features, preserving fine detail through identity long-skip connections, to yield a Lightweight 1.9 M-parameter network. Tumour masks are fed to an Inception ResNet-v2 classifier via a 512-D bottleneck. The pipeline was five-fold cross-validated on 98 contrast-enhanced T1-weighted scans (Memorial Hospital; Ethics 24.12.2021/008) and externally validated on BraTS 2019. On the Memorial cohort UNet SI achieved Dice 0.873, Jaccard 0.853, SSIM 0.992, HD95 24.19 mm. EGFR classification reached Accuracy 0.960, Precision 1.000, Recall 0.871, AUC 0.94, surpassing published state-of-the-art results. Inference time is ≤ 0.18 s per slice on a 4 GB GPU. By combining shearlet-enhanced segmentation with streamlined classification, the DSS delivers superior EGFR prediction and is suitable for integration into routine clinical workflows.