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Item Diffusion Imaging for Tumor Grading of Supratentorial Brain Tumors in the First Year of Life(American Society of Neuroradiology, 2014-04) Kralik, S.F.; Taha, A.; Kamer, A.P.; Cardinal, J.S.; Seltman, T.A.; Ho, C.Y.; Radiology and Imaging Sciences, School of MedicineBackground and purpose: Supratentorial tumors in the first year of life are typically large and heterogeneous at presentation, making differentiation of these CNS neoplasms on pre-operative imaging difficult. We hypothesize that the ADC value can reliably differentiate high- versus low-grade supratentorial tumors in this patient population. Materials and methods: A blinded review of ADC maps was performed on 19 patients with histologically proved supratentorial brain tumors diagnosed within the first year of life. Minimum ADC values obtained by region of interest from 2 neuroradiologists were averaged and compared with World Health Organization tumor grade. ADC values for the entire tumor were also obtained by use of a semi-automated histogram method and compared with World Health Organization tumor grade. Data were analyzed by use of Spearman ρ and Student t test, with a value of P < .05 considered statistically significant. Results: For the manual ADC values, a significant negative correlation was found between the mean minimum ADC and tumor grade (P = .0016). A significant difference was found between the mean minimum ADC of the low-grade (1.14 × 10(-3) mm(2)/s ± 0.30) and high-grade tumors (0.64 × 10(-3) mm(2)/s ± 0.28) (P = .0018). Likewise, the semi-automated method demonstrated a significant negative correlation between the lowest 5th (P = .0002) and 10th (P = .0009) percentile individual tumor ADC values and tumor grade, a significant difference between the mean 5th and 10th percentile ADC values of the low-grade and high-grade groups (P = .0028), and a significant positive correlation with values obtained by manual region-of-interest placement (P < .000001). Conclusions: ADC maps can differentiate high- versus low-grade neoplasms for supratentorial tumors presenting in the first year of life, given the significant negative correlation between ADC values and tumor grade.Item Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma(American Society of Neuroradiology, 2021) Zhang, M.; Wong, S. W.; Lummus, S.; Han, M.; Radmanesh, A.; Ahmadian, S. S.; Prolo, L. M.; Lai, H.; Eghbal, A.; Oztekin, O.; Cheshier, S. H.; Fisher, P. G.; Ho, C. Y.; Vogel, H.; Vitanza, N. A.; Lober, R. M.; Grant, G. A.; Jaju, A.; Yeom, K. W.; Radiology and Imaging Sciences, School of MedicineBackground and purpose: Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes. Materials and methods: We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features. Results: From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI. Conclusions: Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.