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Pontillo G, Tommasin S, Cuocolo R, Petracca M, Petsas N, Ugga L, Carotenuto A, Pozzilli C, Iodice R, Lanzillo R, Quarantelli M, Brescia Morra V, Tedeschi E, Pantano P, Cocozza S. A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis. AJNR Am J Neuroradiol 2021; 42:1927-1933. [PMID: 34531195 DOI: 10.3174/ajnr.a7274] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 07/12/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images. MATERIALS AND METHODS In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. T1-weighted images were processed to obtain volume, connectivity score (inferred from the T2 lesion location), and texture features for an atlas-based set of GM regions. The site 1 cohort was randomly split into training (n = 400) and test (n = 100) sets, while the site 2 cohort (n = 104) constituted the external test set. After feature selection of clinicodemographic and MR imaging-derived variables, different machine learning algorithms predicting disability as measured with the Expanded Disability Status Scale were trained and cross-validated on the training cohort and evaluated on the test sets. The effect of different algorithms on model performance was tested using the 1-way repeated-measures ANOVA. RESULTS The selection procedure identified the 9 most informative variables, including age and secondary-progressive course and a subset of radiomic features extracted from the prefrontal cortex, subcortical GM, and cerebellum. The machine learning models predicted disability with high accuracy (r approaching 0.80) and excellent intra- and intersite generalizability (r ≥ 0.73). The machine learning algorithm had no relevant effect on the performance. CONCLUSIONS The multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability.
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Affiliation(s)
- G Pontillo
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.).,Electrical Engineering and Information Technology (G.P., M.Q.)
| | - S Tommasin
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy
| | - R Cuocolo
- Clinical Medicine and Surgery (R.C.) .,Laboratory of Augmented Reality for Health Monitoring (R.C.)
| | - M Petracca
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - N Petsas
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Mediterraneo (N.P., P.P.), Pozzilli, Italy
| | - L Ugga
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
| | - A Carotenuto
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - C Pozzilli
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy
| | - R Iodice
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - R Lanzillo
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - M Quarantelli
- Electrical Engineering and Information Technology (G.P., M.Q.).,Institute of Biostructure and Bioimaging (M.Q.), National Research Council, Naples, Italy
| | - V Brescia Morra
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - E Tedeschi
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
| | - P Pantano
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy.,Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Mediterraneo (N.P., P.P.), Pozzilli, Italy
| | - S Cocozza
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
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