Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

TLDR

Automated computer analysis offers more objective, reliable, and reproducible brain tumor diagnostics than human readers. The study aims to evaluate pattern classification methods for distinguishing brain tumor types and grading gliomas. A computer‑assisted classification pipeline combining conventional and perfusion MRI was developed, involving ROI definition, extraction of shape, intensity, and rotation‑invariant texture features, recursive feature elimination with SVM, and one‑vs‑all voting for multiclass classification. Binary SVM achieved 85 % accuracy, 87 % sensitivity, and 79 % specificity for metastasis versus glioma, and 88 % accuracy, 85 % sensitivity, and 96 % specificity for high‑grade versus low‑grade gliomas, as assessed by leave‑one‑out cross‑validation.

Abstract

The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer-assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including region-of-interest definition, feature extraction, feature selection, and classification. The extracted features include tumor shape and intensity characteristics, as well as rotation invariant texture features. Feature subset selection is performed using support vector machines with recursive feature elimination. The method was applied on a population of 102 brain tumors histologically diagnosed as metastasis (24), meningiomas (4), gliomas World Health Organization grade II (22), gliomas World Health Organization grade III (18), and glioblastomas (34). The binary support vector machine classification accuracy, sensitivity, and specificity, assessed by leave-one-out cross-validation, were, respectively, 85%, 87%, and 79% for discrimination of metastases from gliomas and 88%, 85%, and 96% for discrimination of high-grade (grades III and IV) from low-grade (grade II) neoplasms. Multiclass classification was also performed via a one-vs-all voting scheme.