Shuai Liu1,2, Xing Fan2, Chuanbao Zhang1, Zheng Wang1,2, Shaowu Li2,3, Yinyan Wang1, Xiaoguang Qiu4, Tao Jiang5,6,7. 1. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 6, Tiantan Xili, Dongcheng District, Beijing, 100050, China. 2. Beijing Neurosurgical Institute, Capital Medical University, Beijing, China. 3. Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 4. Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, No. 6, Tiantan Xili, Dongcheng District, Beijing, 100050, China. ttyy6611@sina.com. 5. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 6, Tiantan Xili, Dongcheng District, Beijing, 100050, China. taojiang1964@163.com. 6. Beijing Neurosurgical Institute, Capital Medical University, Beijing, China. taojiang1964@163.com. 7. Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China. taojiang1964@163.com.
Abstract
OBJECTIVES: The aim of this study was to differentiate primary central nervous system lymphoma (PCNSL) from glioblastomas (GBM) using the fractal analysis of conventional MRI data. MATERIALS AND METHODS: Sixty patients with PCNSL and 107 patients with GBM with MRI data available were enrolled. Fractal dimension (FD) and lacunarity values of the tumour region were calculated using fractal analysis. A predictive model combining fractal parameters and anatomical characteristics was built using logistic regression. The role of FD, lacunarity and the predictive model in differential diagnosis was evaluated using receiver-operating characteristic (ROC) curve analysis. The association between fractal parameters and anatomical characteristics of tumours was also investigated. RESULTS: PCNSL had lower FD values (p < 0.001) and higher lacunarity values (p < 0.001) than GBM. ROC curve analysis revealed that FD, lacunarity, and the predictive model could distinguish PCNSL from GBM (area under the curve: 0.895, 0.776, and 0.969, respectively). The following associations were observed between fractal parameters and anatomical characteristics: multiple lesions were significantly associated with higher lacunarity (p = 0.024), necrosis with higher FD (p = 0.027), corpus callosum involvement with higher lacunarity (p < 0.001) in PCNSL and subventricular zone involvement with higher FD (p < 0.001) in GBM. CONCLUSIONS: The findings of the study indicate that fractal analysis on conventional MRI performs well in distinguishing PCNSL from GBM. KEY POINTS: • Fractal dimension and lacunarity were capable of differentiating PCNSL from GBM. • PCNSL and GBM exhibited different anatomical characteristics. • Fractal parameters were associated with some of these anatomical characteristics.
OBJECTIVES: The aim of this study was to differentiate primary central nervous system lymphoma (PCNSL) from glioblastomas (GBM) using the fractal analysis of conventional MRI data. MATERIALS AND METHODS: Sixty patients with PCNSL and 107 patients with GBM with MRI data available were enrolled. Fractal dimension (FD) and lacunarity values of the tumour region were calculated using fractal analysis. A predictive model combining fractal parameters and anatomical characteristics was built using logistic regression. The role of FD, lacunarity and the predictive model in differential diagnosis was evaluated using receiver-operating characteristic (ROC) curve analysis. The association between fractal parameters and anatomical characteristics of tumours was also investigated. RESULTS:PCNSL had lower FD values (p < 0.001) and higher lacunarity values (p < 0.001) than GBM. ROC curve analysis revealed that FD, lacunarity, and the predictive model could distinguish PCNSL from GBM (area under the curve: 0.895, 0.776, and 0.969, respectively). The following associations were observed between fractal parameters and anatomical characteristics: multiple lesions were significantly associated with higher lacunarity (p = 0.024), necrosis with higher FD (p = 0.027), corpus callosum involvement with higher lacunarity (p < 0.001) in PCNSL and subventricular zone involvement with higher FD (p < 0.001) in GBM. CONCLUSIONS: The findings of the study indicate that fractal analysis on conventional MRI performs well in distinguishing PCNSL from GBM. KEY POINTS: • Fractal dimension and lacunarity were capable of differentiating PCNSL from GBM. • PCNSL and GBM exhibited different anatomical characteristics. • Fractal parameters were associated with some of these anatomical characteristics.
Entities:
Keywords:
Diagnosis; Fractals; Glioblastoma; Lymphoma; Magnetic resonance imaging
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