Semiautomatic classification of benign and malignant vertebral fractures in magnetic resonance imaging

Author(s)

Lucas Frighetto Pereira

• Document type: Master's Dissertation
• Press: Ribeirão Preto.
• Institution: Universidade de São Paulo (USP). Faculdade de Medicina de Ribeirão Preto (PCARP/BC)
• Defense date: 2016-12-05

Examining board members

Marcello Henrique Nogueira Barbosa; Carlos Fernando Pereira da Silva Herrero; Agma Juci Machado Traina

• Advisor: Marcello Henrique Nogueira Barbosa

Abstract

Purpose: Vertebral compression fractures (VCFs) result in partial collapse of vertebral bodies. They usually are nontraumatic or occur with low-energy trauma in the elderly secondary to different etiologies, such as insufficiency fractures of bone fragility in osteoporosis (benign fractures) or vertebral metastasis (malignant fractures). Our study aims to detect the presence of VCFs and classify them as malignant and benign using image processing techniques and machine learning classifiers in T1-weighted magnetic resonance images (MRI). Materials and methods: We used the median sagittal planes of lumbar spine MRIs from 63 patients (38 women and 25 men) previously diagnosed with VCFs. The lumbar vertebral bodies were manually segmented and statistical features of gray levels were computed from the histogram. We also extracted texture features to analyze the gray-level distribution, and shape features to analyze the contours of the vertebral bodies. In total, 102 lumbar VCFs (53 benign and 49 malignant) and 89 normal lumbar vertebral bodies were analyzed. After run feature selection methods to the vector of features, the k-nearest-neighbor (k-NN), neural network with radial basis functions (RBF network), a naïve Bayes classifier, J48, and Support Vector Machine (SVM) were used for classification. We compared the classification obtained by these classifiers with the final diagnosis of each case, including biopsy for the malignant fractures and clinical and laboratory follow up for the benign fractures. Furthermore, three voluntary radiologists classified the same cases analyzing the same regions of interests (ROIs) used by the classifiers and a comparison between the classifiers and the radiologists was done. xxiii Results: The results obtained by the classifiers showed an area under the receiver operating characteristic curve (AUROC) of 0.984 in distinguishing between normal and fractured vertebral bodies, and AUROC of 0.930 in discriminating between benign and malignant VCFs. Conclusion: Our method reached great results in the classification of vertebral bodies without fractures, vertebral bodies with fractures due to osteoporosis and vertebral bodies with fractures due to metastatic diseases. Our results were statistically equivalent to the results of the classifications made by radiologists and they showed to be promising in diagnosis assisting of VCFs. (AU)

• FAPESP's process: 14/12135-0 - Semiautomatic classification of benign and malignant vertebral fractures in magnetic resonance imaging
• Grantee: Lucas Frighetto Pereira
• Support Opportunities: Scholarships in Brazil - Master