Evaluating the Gray Level Co-Occurrence Matrix-Based Texture Features of Magnetic Resonance Images for Glioblastoma Multiform Patients' Treatment Response Assessment

Sanaz Alibabaei; Masoumeh Rahmani; Marziyeh Tahmasbi; Mohammad Javad Tahmasebi Birgani; Sasan Razmjoo

doi:10.4103/jmss.jmss_50_22

Evaluating the Gray Level Co-Occurrence Matrix-Based Texture Features of Magnetic Resonance Images for Glioblastoma Multiform Patients' Treatment Response Assessment

J Med Signals Sens. 2023 Aug 31;13(4):261-271. doi: 10.4103/jmss.jmss_50_22. eCollection 2023 Oct-Dec.

Authors

Sanaz Alibabaei¹, Masoumeh Rahmani², Marziyeh Tahmasbi³, Mohammad Javad Tahmasebi Birgani⁴, Sasan Razmjoo⁵

Affiliations

¹ Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
² Department of Biomedical Engineering, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
³ Department Radiologic Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
⁴ Department of Medical Physics, Faculty of Medicine, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
⁵ Department of Clinical Oncology and Clinical Research Development Center, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

Abstract

Background: Medical images of cancer patients are usually evaluated qualitatively by clinical specialists which makes the accuracy of the diagnosis subjective and related to the skills of clinicians. Quantitative methods based on the textural feature analysis may be useful to facilitate such evaluations. This study aimed to analyze the gray level co-occurrence matrix (GLCM)-based texture features extracted from T1-axial magnetic resonance (MR) images of glioblastoma multiform (GBM) patients to determine the distinctive features specific to treatment response or disease progression.

Methods: 20 GLCM-based texture features, in addition to mean, standard deviation, entropy, RMS, kurtosis, and skewness were extracted from step I MR images (obtained 72 h after surgery) and step II MR images (obtained three months later). Responded and not responded patients to treatment were classified manually based on the radiological evaluation of step II images. Extracted texture features from Step I and Step II images were analyzed to determine the distinctive features for each group of responsive or progressive diseases. MATLAB 2020 was applied to feature extraction. SPSS version 26 was used for the statistical analysis. P value < 0.05 was considered statistically significant.

Results: Despite no statistically significant differences between Step I texture features for two considered groups, almost all step II extracted GLCM-based texture features in addition to entropy M and skewness were significantly different between responsive and progressive disease groups.

Conclusions: GLCM-based texture features extracted from MR images of GBM patients can be used with automatic algorithms for the expeditious prediction or interpretation of response to the treatment quantitatively besides qualitative evaluations.

Keywords: Glioblastoma multiform; gray level co-occurrence matrix; texture feature; treatment response.