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Added value of synthetic T1/T2-weighted MR images in the segmentation and staging of meningioma

Authors :

Masoud Noroozi¹ Sayna Jamaati² Peyman Sharifian³ Mahsa Karbasi⁴ Esmaeil Gharepapagh⁵ Alireza Karimian⁶ Hossein Arabi⁷ Sahar Rezaei⁸

1- گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه اصفهان 2- دانشگاه صنعتی شریف 3- گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه اصفهان 4- دانشگاه علوم پزشکی تبریز 5- دانشگاه علوم پزشکی تبریز 6- گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه اصفهان 7- دانشگاه ژنو 8- دانشگاه علوم پزشکی تبریز

Keywords :

Meningioma،T1/T2 Ratio Imaging،nnU-Net،Segmentation،Automated WHO Grading،Deep Learning

Abstract :

Accurate pre-operative classification and volumetric definition of intracranial meningiomas are paramount to the development of appropriate surveillance, surgical, and radiotherapeutic approaches. The traditional post-contrast T1-weighted MRI (T1c) is used clinically but is time-consuming to contour and not always available or against indications. To assess how a bias-corrected native-T1/ T2-weighted ratio (T1(n)/T2(w)) map, combined with fully automated segmentation and grading networks, can enhance meningioma work-up without gadolinium. The novelty of this research lies in the use of T1n/T2w information to generate synthetic images, replacing the need for four separate MRI sequences. The BraTS-MEN multi-centre dataset (685 scans to be segmented and 868 scans to be graded) was skull-stripped and registered to the atlas. A 3-D nnU-Net V2 was trained to segment tumors using (i) T1c and (ii) T1n/T2w volumes. The resulting masks were either presented directly or supplemented with the four mpMRI channels in a 3-D ResNet-18 to predict WHO grades 1-2. Performance was measured as Dice, IoU, accuracy, and class-based sensitivity/specificity. T1c performed the best in terms of geometry Dice (92.12 ± 4.14 %) and IoU (86.35 ± 11.3 %). The T1n/T2w map nonetheless maintained a clinically satisfactory Dice of 82.1 ± 11.68 with decreased false-positive voxels in neighboring dura. The ratio-based pipeline was superior to the T1c model in all global measures (accuracy 0.61 vs 0.427; mean Dice 0.558 vs 0.425) and the sensitivity of high-grade (>= WHO II) tumor was over twofold higher (0.70 vs 0.31). The T1n/T2w ratio map, as a gadolinium-free contrast agent, and the nnU-Net V2 in segmentation and ResNet-18 in the classifier demonstrate strength in automated grade assessment accuracy and preventing the under-treatment of aggressive meningiomas. An open-source, hardware-light, and easily reproducible workflow indicates a potential avenue of non-invasive, contrast-sparing pre-operative assessment that could be validated in a multi-institutional prospective setting.