PT  - JOURNAL ARTICLE
AU  - Zhang, M.
AU  - Wong, S.W.
AU  - Lummus, S.
AU  - Han, M.
AU  - Radmanesh, A.
AU  - Ahmadian, S.S.
AU  - Prolo, L.M.
AU  - Lai, H.
AU  - Eghbal, A.
AU  - Oztekin, O.
AU  - Cheshier, S.H.
AU  - Fisher, P.G.
AU  - Ho, C.Y.
AU  - Vogel, H.
AU  - Vitanza, N.A.
AU  - Lober, R.M.
AU  - Grant, G.A.
AU  - Jaju, A.
AU  - Yeom, K.W.
TI  - Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma
AID  - 10.3174/ajnr.A7200
DP  - 2021 Sep 01
TA  - American Journal of Neuroradiology
PG  - 1702--1708
VI  - 42
IP  - 9
4099  - http://www.ajnr.org/content/42/9/1702.short
4100  - http://www.ajnr.org/content/42/9/1702.full
SO  - Am. J. Neuroradiol.2021 Sep 01; 42
AB  - BACKGROUND 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.ATRTatypical teratoid/rhabdoid tumorAUCarea under the curveGLCMgray level co-occurrence matrixMBmedulloblastoma