M Zhang1, S W Wong2, S Lummus3, M Han4, A Radmanesh5, S S Ahmadian6, L M Prolo7, H Lai8, A Eghbal8, O Oztekin9,10, S H Cheshier11, P G Fisher12, C Y Ho13, H Vogel6, N A Vitanza14, R M Lober15, G A Grant7, A Jaju16, K W Yeom17. 1. From the Departments of Neurosurgery (M.Z.). 2. Department of Statistics (S.W.W.), Stanford University, Stanford, California. 3. Department of Physiology and Nutrition (S.L.), University of Colorado, Colorado Springs, Colorado. 4. Department of Pediatrics (M.H.), Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania. 5. Department of Radiology (A.R.), New York University Grossman School of Medicine, New York, New York. 6. Pathology (S.S.A., H.V.), Stanford Medical Center, Stanford University, Stanford, California. 7. Departments of Neurosurgery (L.M.P., G.A.G.). 8. Department of Radiology (H.L., A.E.), Children's Hospital of Orange County, Orange, California and University of California, Irvine, Irvine, California. 9. Department of Neuroradiology (O.O.), Cigli Education and Research Hospital, Bakircay University, Izmir, Turkey. 10. Department of Neuroradiology (O.O.), Tepecik Education and Research Hospital, Health Science University, Izmir, Turkey. 11. Division of Pediatric Neurosurgery (S.H.C.), Department of Neurosurgery, Huntsman Cancer Institute, Intermountain Healthcare Primary Children's Hospital, University of Utah School of Medicine, Salt Lake City, Utah. 12. Neurology (P.G.F.). 13. Departments of Clinical Radiology & Imaging Sciences (C.Y.H.), Riley Children's Hospital, Indiana University, Indianapolis, Indiana. 14. Division of Pediatric Hematology/Oncology (N.A.V.), Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington. 15. Division of Neurosurgery (R.M.L.), Department of Pediatrics, Wright State University Boonshoft School of Medicine, Dayton Children's Hospital, Dayton, Ohio. 16. Department of Medical Imaging (A.J.), Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois. 17. Radiology (K.W.Y.), Lucile Packard Children's Hospital, Stanford University, Palo Alto, California kyeom@stanford.edu.
Abstract
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.
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.
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Authors: Sarah A Mattonen; Dev Gude; Sebastian Echegaray; Shaimaa Bakr; Daniel L Rubin; Sandy Napel Journal: J Med Imaging (Bellingham) Date: 2020-03-14
Authors: M Zhang; L Tam; J Wright; M Mohammadzadeh; M Han; E Chen; M Wagner; J Nemalka; H Lai; A Eghbal; C Y Ho; R M Lober; S H Cheshier; N A Vitanza; G A Grant; L M Prolo; K W Yeom; A Jaju Journal: AJNR Am J Neuroradiol Date: 2022-03-31 Impact factor: 3.825