Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma
| Autor: | Samuel H. Cheshier, C.Y. Ho, Kristen W. Yeom, Alireza Radmanesh, Alok Jaju, Gerald A. Grant, Paul G. Fisher, L.M. Prolo, S.S. Ahmadian, Azam A Eghbal, Hollie Lai, Hannes Vogel, S.W. Wong, Robert M. Lober, S. Lummus, Ozgur Oztekin, Michelle Han, Nick Vitanza, Michael Zhang |
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| Rok vydání: | 2021 |
| Předmět: |
Medulloblastoma
medicine.medical_specialty Imaging biomarker business.industry Rhabdoid tumors Logistic regression medicine.disease Magnetic Resonance Imaging Pediatrics Phenotype Text mining Radiomics Humans Medicine Radiology Nuclear Medicine and imaging Neurology (clinical) Radiology Cerebellar Neoplasms business Rhabdoid Tumor Retrospective Studies Sparse regression |
| Zdroj: | AJNR Am J Neuroradiol |
| ISSN: | 1936-959X 0195-6108 |
| DOI: | 10.3174/ajnr.a7200 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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