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 M. Zhang
A1 S.W. Wong
A1 S. Lummus
A1 M. Han
A1 A. Radmanesh
A1 S.S. Ahmadian
A1 L.M. Prolo
A1 H. Lai
A1 A. Eghbal
A1 O. Oztekin
A1 S.H. Cheshier
A1 P.G. Fisher
A1 C.Y. Ho
A1 H. Vogel
A1 N.A. Vitanza
A1 R.M. Lober
A1 G.A. Grant
A1 A. Jaju
A1 K.W. Yeom
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