Biomaterials used for stabilization of compressed vertebraes due to osteoporosis, have mainly been based on resin materials, like PMMA (polymethyl methacrylate), but have recently expanded to consist of injectable ceramics, such as calcium-aluminate. In this in vitro study human osteoblast-like cells, MG-63, were cultured on three different injectable biomaterials based on: Ca-aluminate, Bis-GMA (bisphenol A-glycidylmethacrylate) and PMMA, to investigate the cellular response elicited by these materials. Cell proliferation was measured by the NucleoCounter® system, cell viability was investigated by LDH (lactate dehydrogenase) analysis, cell differentiation and mineralization was evaluated by mRNA gene expression of the osteoblastic markers: ALP (alkaline phosphatase), OC (osteocalcin) and COLL-I (collagen type I) by qPCR (quantitative polymerase chain reaction) analysis. Two control materials were used: TCP (tissue culture polystyrene, negative control) and PVC (polyvinyl chloride, positive control). The results showed that all the bone cement materials were non-toxic and biocompatible, i.e. they provided good cell viability and proliferation of the MG-63 cells. They are specific for bone cells, since they expressed high values of the osteoblast-specific differentiation markers, and are thus promising as injectable bone cement materials. Among the bone cements, Xeraspine appears to be the most biocompatible material for bone cells. It is followed by Cortoss and then Vertebroplastic.

<p>Biomaterials used for stabilization of compressed vertebraes due to osteoporosis, have mainly been based on resin materials, like PMMA (polymethyl methacrylate), but have recently expanded to consist of injectable ceramics, such as calcium-aluminate. In this in vitro study human osteoblast-like cells, MG-63, were cultured on three different injectable biomaterials based on: Ca-aluminate, Bis-GMA (bisphenol A-glycidylmethacrylate) and PMMA, to investigate the cellular response elicited by these materials. Cell proliferation was measured by the NucleoCounter® system, cell viability was investigated by LDH (lactate dehydrogenase) analysis, cell differentiation and mineralization was evaluated by mRNA gene expression of the osteoblastic markers: ALP (alkaline phosphatase), OC (osteocalcin) and COLL-I (collagen type I) by qPCR (quantitative polymerase chain reaction) analysis. Two control materials were used: TCP (tissue culture polystyrene, negative control) and PVC (polyvinyl chloride, positive control). The results showed that all the bone cement materials were non-toxic and biocompatible, i.e. they provided good cell viability and proliferation of the MG-63 cells. They are specific for bone cells, since they expressed high values of the osteoblast-specific differentiation markers, and are thus promising as injectable bone cement materials. Among the bone cements, Xeraspine appears to be the most biocompatible material for bone cells. It is followed by Cortoss and then Vertebroplastic.</p>

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Propósito: Fraturas vertebrais por compressão (FVCs) são caracterizadas por colapso parcial de corpos vertebrais. Elas tipicamente ocorrem na população idosa de forma não traumática ou por trauma de baixa energia, podendo ser secundárias a fragilidade causada pela osteoporose (FVCs benignas) ou metástases vertebrais (FVCs malignas). Nosso trabalho tem o objetivo de detectar a presença de FVCs e de classifica-las como FVC maligna ou FVC benigna utilizando técnicas de processamento de imagens e aprendizado de máquinas em imagens ponderadas em T1 obtidas em ressonância magnética (RM). Materiais e Métodos: Foram utilizados os planos sagitais medianos das RMs da coluna lombar de 63 pacientes (38 mulheres e 25 homens) previamente diagnosticados com FVCs. Os corpos vertebrais lombares foram segmentados manualmente. Atributos de análise de níveis de cinza foram calculados do histograma dos corpos vertebrais. Foram extraídos também atributos de textura para analisar a distribuição dos níveis de cinza e atributos de forma para analisar o formato dos corpos vertebrais. No total, 102 FVCs lombares (53 benignas e 49 malignas) e 89 corpos vertebrais lombares foram analisados. Após a aplicação de métodos de seleção de atributos nos vetores de características, foram realizadas classificações com os classificadores k-nearest-neighbor (k-NN), uma rede neural artificial com função de base radial (RBF network), naïve Bayes, J48 e Support Vector Machine (SVM). O padrão de referência para calcular o desempenho diagnóstico do sistema desenvolvido foi uma classificação obtida do prontuário médico eletrônico com o diagnóstico final de cada caso, incluindo no mínimo informações a respeito de xxi biópsia para as FVC lesões malignas e acompanhamento clínico e laboratorial para as FVCs benignas. Três radiologistas classificaram os mesmos casos analisando as mesmas regiões de interesse (ROIs) que os classificadores e uma comparação entre classificadores e radiologistas foi realizada. Resultados: Os resultados obtidos pelos classificadores mostraram uma área abaixo da curva receiver operating characteristic (AUROC) de 0,984 para distinguir entre corpos vertebrais com FVC e normais e AUROC de 0,930 para discriminar entre FVC benigna e FVC maligna. Conclusão: Nosso método alcançou ótimos resultados na classificação de corpos vertebrais sem fratura, corpos vertebrais com fratura por osteoporose e corpos vertebrais com fraturas secundárias a doença metastática. Nossos resultados foram estatisticamente equivalentes ao de médicos radiologistas e se mostraram promissores na assistência em diagnóstico de FVCs. / 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.

The use of injectable biomaterials is growing as the demands for minimally invasive procedures, and more easily applicable implants become higher, but their availability is still limited due to the difficulties associated to their design. Each year, more than 700,000 vertebral compression fractures (VCF’s) are reported in the US and 500,000 VCF’s in Europe due to primary osteoporosis only. VCF’s can compromise the delicacy of the spinal canal and also cause back pain, which affects the patient’s quality of life. Vertebroplasty was developed in the 80’s, and has proven to be a safe minimally invasive procedure that can, quickly and sustainably, relieve the pain in patients experiencing VCF’s. However, biomaterials for vertebroplasty still have limitations. For instance, ceramic bone cements are difficult to distinguish from the bone using X-ray techniques. On the other hand, acrylic bone cements may cause adjacent vertebral fractures (AVF’s). Large clinical studies have indicated that 12 to 20% vertebroplasty recipients developed subsequent vertebral fractures, and that 41 to 67% of these, were AVF’s. This may be attributed to the load shifting and increased pressure on the adjacent endplates reached after vertebroplasty with stiff cements. The primary aim of this thesis was to develop better injectable biomaterials for spinal applications, particularly, bone cements for vertebroplasty. Water-soluble radiopacifiers were first investigated to enhance the radiopacity of resorbable ceramic cements. Additionally, different strategies to produce materials that mechanically comply with the surrounding tissues (low-modulus bone cements) were investigated. When a suitable low-modulus cement was produced, its performance was evaluated in both bovine bone, and human vertebra ex vivo models. In summary, strontium halides showed potential as water-soluble radiocontrast agents and could be used in resorbable calcium phosphates and other types of resorbable biomaterials. Conversely, linoleic acid-modified (low-modulus) cements appeared to be a promising alternative to currently available high-modulus cements. It was also shown that the influence of the cement properties on the strength and stiffness of a single vertebra depend upon the initial bone volume fraction, and that at low bone volume fractions, the initial mechanical properties of the vertebroplasty cement become more relevant. Finally, it was shown that vertebroplasty with low-modulus cements is biomechanically safe, and could become a recommended minimally invasive therapy in selected cases, especially for patients suffering from vertebral compression fractures due to osteoporosis.

Propósito: Fraturas vertebrais por compressão (FVCs) são caracterizadas por colapso parcial de corpos vertebrais. Elas tipicamente ocorrem na população idosa de forma não traumática ou por trauma de baixa energia, podendo ser secundárias a fragilidade causada pela osteoporose (FVCs benignas) ou metástases vertebrais (FVCs malignas). Nosso trabalho tem o objetivo de detectar a presença de FVCs e de classifica-las como FVC maligna ou FVC benigna utilizando técnicas de processamento de imagens e aprendizado de máquinas em imagens ponderadas em T1 obtidas em ressonância magnética (RM). Materiais e Métodos: Foram utilizados os planos sagitais medianos das RMs da coluna lombar de 63 pacientes (38 mulheres e 25 homens) previamente diagnosticados com FVCs. Os corpos vertebrais lombares foram segmentados manualmente. Atributos de análise de níveis de cinza foram calculados do histograma dos corpos vertebrais. Foram extraídos também atributos de textura para analisar a distribuição dos níveis de cinza e atributos de forma para analisar o formato dos corpos vertebrais. No total, 102 FVCs lombares (53 benignas e 49 malignas) e 89 corpos vertebrais lombares foram analisados. Após a aplicação de métodos de seleção de atributos nos vetores de características, foram realizadas classificações com os classificadores k-nearest-neighbor (k-NN), uma rede neural artificial com função de base radial (RBF network), naïve Bayes, J48 e Support Vector Machine (SVM). O padrão de referência para calcular o desempenho diagnóstico do sistema desenvolvido foi uma classificação obtida do prontuário médico eletrônico com o diagnóstico final de cada caso, incluindo no mínimo informações a respeito de xxi biópsia para as FVC lesões malignas e acompanhamento clínico e laboratorial para as FVCs benignas. Três radiologistas classificaram os mesmos casos analisando as mesmas regiões de interesse (ROIs) que os classificadores e uma comparação entre classificadores e radiologistas foi realizada. Resultados: Os resultados obtidos pelos classificadores mostraram uma área abaixo da curva receiver operating characteristic (AUROC) de 0,984 para distinguir entre corpos vertebrais com FVC e normais e AUROC de 0,930 para discriminar entre FVC benigna e FVC maligna. Conclusão: Nosso método alcançou ótimos resultados na classificação de corpos vertebrais sem fratura, corpos vertebrais com fratura por osteoporose e corpos vertebrais com fraturas secundárias a doença metastática. Nossos resultados foram estatisticamente equivalentes ao de médicos radiologistas e se mostraram promissores na assistência em diagnóstico de FVCs. / 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.