Texture analysis of high resolution MRI allows discrimination between febrile and afebrile initial precipitating injury in mesial temporal sclerosis

Author(s)

Alegro, Maryana de Carvalho ^{[1, 2]} ; Silva, Alexandre Valotta ^{[3, 2]} ; Bando, Silvia Yumi ^[4] ; Lopes, Roseli de Deus ^[1] ; Martins de Castro, Luiz Henrique ^[5] ; HungTsu, Wen ^[5] ; Moreira-Filho, Carlos Alberto ^[4] ; Amaro, Jr., Edson ^{[2, 6]}

Affiliation

^[1] Univ Sao Paulo, Escola Politecn, Integrated Syst Lab, Brain Inst, Dep Radiol, Hosp Israelita Albert Einst, Sao Paulo - Brazil

^[2] Hosp Israelita Albert Einstein, Inst Brain, Dept Radiol, BR-05652900 Sao Paulo - Brazil

^[3] Univ Fed Sao Paulo, Dept Biosci, Sao Paulo - Brazil

^[4] FMUSP, Dept Pediat, Sao Paulo - Brazil

^[5] FMUSP, Dept Neurol, Sao Paulo - Brazil

^[6] FMUSP, Dept Radiol & Oncol, Sao Paulo - Brazil

• Document type: Journal article
• Source: MAGNETIC RESONANCE IN MEDICINE; v. 68, n. 5, p. 1647-1653, NOV 2012.
• Web of Science Citations: 11

Abstract

A computational pipeline combining texture analysis and pattern classification algorithms was developed for investigating associations between high-resolution MRI features and histological data. This methodology was tested in the study of dentate gyrus images of sclerotic hippocampi resected from refractory epilepsy patients. Images were acquired using a simple surface coil in a 3.0T MRI scanner. All specimens were subsequently submitted to histological semiquantitative evaluation. The computational pipeline was applied for classifying pixels according to: a) dentate gyrus histological parameters and b) patients' febrile or afebrile initial precipitating insult history. The pipeline results for febrile and afebrile patients achieved 70% classification accuracy, with 78% sensitivity and 80% specificity {[}area under the reader observer characteristics (ROC) curve: 0.89]. The analysis of the histological data alone was not sufficient to achieve significant power to separate febrile and afebrile groups. Interesting enough, the results from our approach did not show significant correlation with histological parameters (which per se were not enough to classify patient groups). These results showed the potential of adding computational texture analysis together with classification methods for detecting subtle MRI signal differences, a method sufficient to provide good clinical classification. A wide range of applications of this pipeline can also be used in other areas of medical imaging. Magn Reson Med, 2012. (c) 2012 Wiley Periodicals, Inc. (AU)