Kim W, et al. J Korean Med Sci. 2014 Sep;29(9):1278-1286. https://doi.org/10.3346/jkms.2014.29.9.1278

Background: This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in glioma compared with neuroradiologists. Methods: For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospitals and The Cancer Imaging Archive. Performance was tested on internal (119, 78 IDH-wildtype, 41 IDH-mutant [IDH1 and 2]) and external test sets (108, 72 IDH-wildtype, 36 IDH-mutant). GAA was developed using a score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with the null model. Two neuroradiologists (R1, R2) assessed realism, diversity of imaging phenotypes, and predicted IDH mutation. The performance of a classifier trained with optimal GAA was compared with that of neuroradiologists using the area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested. Results: Generated images demonstrated realism (Turing’s test: 47.5–50.5%) and diversity indicating IDH type. Optimal GAA was achieved with augmentation with 110 000 generated slices (AUC: 0.938). The classifier trained with optimal GAA demonstrated significantly higher AUC values than neuroradiologists in both the internal (R1, P = .003; R2, P < .001) and external test sets (R1, P < .01; R2, P < .001). GAA with large-sized tumors or predominant enhancement showed comparable performance to optimal GAA (internal test: AUC 0.956 and 0.922; external test: 0.810 and 0.749). Conclusions: The application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma.

Lee HS, et al. Korean J Neurotrauma. 2018 Oct;14(2):68-75. https://doi.org/10.13004/kjnt.2018.14.2.68

Jang SW, et al. Brain Tumor Res Treat. 2022 Oct;10(4):255-264. https://doi.org/10.14791/btrt.2022.0035

Background: This study validates MRI-based tumor habitats in predicting time-to-progression (TTP), overall survival (OS), and progression sites in isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients. Methods: Seventy-nine patients were prospectively enrolled between January 2020 and June 2022. MRI, including diffusion-weighted and dynamic susceptibility contrast imaging, were obtained immediately postoperation and at three serial timepoints. Voxels from cerebral blood volume and apparent diffusion coefficient maps were grouped into three habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue) using k-means clustering. Predefined cutoffs for increases in hypervascular and hypovascular cellular habitat were applied to calculate the habitat risk score. Associations between spatiotemporal habitats, habitat risk score, TTP, and OS were investigated using Cox proportional hazards modeling. Habitat risk score was compared to tumor volume using time-dependent receiver operating characteristics analysis. Progression sites were matched with spatial habitats. Results: Increases in hypervascular and hypovascular cellular habitats and habitat risk scores were associated with shorter TTP and OS (all P < .05). Hypovascular cellular habitat and habitat risk scores 1 and 2 independently predicted TTP (hazard ratio [HR], 4.14; P = .03, HR, 4.51; P = .001 and HR, 10.02; P < .001, respectively). Hypovascular cellular habitat and habitat risk score 2 independently predicted OS (HR, 4.01, P = .003; and HR, 3.27, P < .001, respectively). Habitat risk score outperformed tumor volume in predicting TTP (12-month AUC, 0.762 vs. 0.646, P = .048). Hypovascular cellular habitat predicted progression sites (mean Dice index: 0.31). Conclusions: Multiparametric physiologic MRI-based spatiotemporal tumor habitats and habitat risk scores are useful biomarkers for early tumor progression and outcomes in IDH-wildtype glioblastoma patients.

To spatially validate intratumoral subregions (tumor habitat) using physiologic MRI on pathology of the isocitrate dehydrogenase (IDH)-wildtype whole-glioblastoma sample. Data of 20 patients (168 slides) were obtained from the Ivy Glioblastoma Atlas Project. On MRI, tumor habitats were defined using voxel-wise clustering of apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps for contrast-enhancing lesion (CEL) and non-enhancing lesion (NEL). On pathology slides, normalized areas of leading edge (LE), infiltrating tumor (IT), cellular tumor (CT), hypervascular lesion (CThypervascular), and perinecrotic lesion (CTperinecrotic) were obtained. Gross specimen was co-registered on MRI and correlation between pathology-MRI habitats was calculated. RNA sequencing of 67 samples was assessed using 4 Neftel subtypes and further correlated with pathology. Six tumor habitats were identified: hypervascular, hypovascular cellular, and hypovascular hypocellular habitats for CEL and NEL. CT was correlated with hypovascular cellular habitat in CEL (r= 0.238, p =.005). IT was correlated with hypovascular cellular habitat in NEL (r= 0.294, p =.017). CThypervascular was correlated with hypervascular habitat in NEL (r= 0.195, p = .023). CTperinecrotic was correlated with imaging necrosis (r= 0.199, p =.005). Astrocyte-like subtypes were correlated with IT (r= 0.256, p <.001), while mesenchymal-like subtypes were correlated with CTperinecrotic area (r= 0.246, p <.001). Pathologically matched tumor subregions were cellular tumor with hypovascular cellular habitat in CEL and infiltrative tumor with hypovascular cellular habitat in NEL. Identification of the most aggressive as well as infiltrative tumor portion can be achieved using non-invasive MRI tumor habitats.