M Zhang1, L Tam2, J Wright3,4, M Mohammadzadeh5, M Han6, E Chen7, M Wagner8, J Nemalka9, H Lai10, A Eghbal10, C Y Ho7, R M Lober11, S H Cheshier9, N A Vitanza12, G A Grant13, L M Prolo13, K W Yeom14, A Jaju15. 1. From the Departments of Neurosurgery (M.Z.). 2. Stanford University School of Medicine (L.T.), Stanford, California. 3. Department of Radiology (J.W.). 4. Department of Radiology (J.W.), Harborview Medical Center, Seattle,Washington. 5. Department of Radiology (M.M.), Tehran University of Medical Sciences, Tehran, Iran. 6. Department of Pediatrics (M.H.), Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania. 7. Departments of Clinical Radiology & Imaging Sciences (E.C., C.Y.H.), Riley Children's Hospital, Indiana University, Indianapolis, Indiana. 8. Department of Diagnostic Imaging (M.W.), The Hospital for Sick Children, Ontario, Canada. 9. Division of Pediatric Neurosurgery (J.N., 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. 10. Department of Radiology (H.L., A.E.), CHOC Children's Hospital of Orange County California, University of California, Irvine, California. 11. Division of Neurosurgery (R.M.L.), Dayton Children's Hospital, Dayton, Ohio; Department of Pediatrics, Wright State University Boonshoft School of Medicine, Dayton, Ohio. 12. Division of Pediatric Hematology/Oncology (N.A.V.), and Department of Pediatrics, Seattle Children's Hospital, Seattle,Washington. 13. Neurosurgery (G.A.G., L.M.P.), Lucile Packard Children's Hospital, Stanford University, Palo Alto, California. 14. Departments of Radiology (K.W.Y.). 15. Department of Medical Imaging (A.J.), Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois ajaju@luriechildrens.org.
Abstract
BACKGROUND AND PURPOSE: Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging-based radiomics phenotypes that can differentiate these tumor types. MATERIALS AND METHODS: Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative-based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio. RESULTS: The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively. CONCLUSIONS: In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.
BACKGROUND AND PURPOSE: Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging-based radiomics phenotypes that can differentiate these tumor types. MATERIALS AND METHODS: Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative-based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio. RESULTS: The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively. CONCLUSIONS: In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.
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