RT Journal Article
SR Electronic
T1 Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1702
OP 1708
DO 10.3174/ajnr.A7200
VO 42
IS 9
A1 Zhang, M.
A1 Wong, S.W.
A1 Lummus, S.
A1 Han, M.
A1 Radmanesh, A.
A1 Ahmadian, S.S.
A1 Prolo, L.M.
A1 Lai, H.
A1 Eghbal, A.
A1 Oztekin, O.
A1 Cheshier, S.H.
A1 Fisher, P.G.
A1 Ho, C.Y.
A1 Vogel, H.
A1 Vitanza, N.A.
A1 Lober, R.M.
A1 Grant, G.A.
A1 Jaju, A.
A1 Yeom, K.W.
YR 2021
UL http://www.ajnr.org/content/42/9/1702.abstract
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