Katarina Petrujkić1, Nebojša Milošević2, Nemanja Rajković2, Dejana Stanisavljević3, Svetlana Gavrilović4, Dragana Dželebdžić4, Rosanda Ilić5, Antonio Di Ieva6, Ružica Maksimović7. 1. Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia. Electronic address: r_katarina@yahoo.com. 2. Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia. 3. Department for Medical Statistics, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia. 4. Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia. 5. Department of Neurosurgery, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia; Clinical Centre of Serbia, Clinical for Neurosurgery, Dr Koste Todorovića 54, 11000 Belgrade, Serbia. 6. Department of Clinical Medicine, Faculty of Medicine and Health Science, Neurosurgery Unit, Macquarie University, 2 Technology Place, Macquarie University, Sydney, NSW 2109, Australia. 7. Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia; Department of Radiology, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia.
Abstract
PURPOSE: Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. METHOD: In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. RESULTS: All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. CONCLUSIONS: Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.
PURPOSE:Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. METHOD: In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. RESULTS: All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. CONCLUSIONS: Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.
Authors: Francesca Pasi; Marco G Persico; Manuela Marenco; Martina Vigorito; Angelica Facoetti; Marina Hodolic; Rosanna Nano; Giorgio Cavenaghi; Lorenzo Lodola; Carlo Aprile Journal: Front Neurosci Date: 2020-11-13 Impact factor: 4.677
Authors: Santiago Cepeda; Sergio García-García; Ignacio Arrese; Gabriel Fernández-Pérez; María Velasco-Casares; Manuel Fajardo-Puentes; Tomás Zamora; Rosario Sarabia Journal: Front Oncol Date: 2021-02-02 Impact factor: 6.244