RESUMO
Although feline mammary carcinoma is not the most prevalent among the species, its aggressive behavior represents a low life expectancy, compared with most undifferentiated types of breast cancer. Tissue stiffness induced by the accumulation of collagen fibers is related to a risk factor for carcinogenesis in healthy women and aggressiveness in those with breast cancer, which can also occur in cats. The objective of this work is to identify the relationship between stromal collagen density and aggressiveness of mammary carcinoma in cats, according to the peripheral and central tissue distribution by the Picrossirius Red histochemical method. Image.J® and MatLab® software were used for digital image processing. The mean values of kurtosis and entropy attributes were grouped into a control group, and low and high-grade carcinoma groups, analyzed with one-way ANOVA and Bonferronis multiple comparison test (p <0.01). Interpretation of stromal dynamics is important to evaluate both central and peripheral locations. According to entropy, there was a significant increase in the peripheral density in the carcinoma groups in relation to the control group, which can be justified by blood support. The same can be said of the central region, with a significant gain in collagen fibers from the tumors, indicated by kurtosis. The results suggest the presence of increases in stromal density in mammary carcinomas of cats, regardless of their graduation, and occurring in both regions.
Assuntos
Animais , Gatos , Colágeno/análise , Gatos , Imuno-Histoquímica , Neoplasias Mamárias AnimaisRESUMO
Although feline mammary carcinoma is not the most prevalent among the species, its aggressive behavior represents a low life expectancy, compared with most undifferentiated types of breast cancer. Tissue stiffness induced by the accumulation of collagen fibers is related to a risk factor for carcinogenesis in healthy women and aggressiveness in those with breast cancer, which can also occur in cats. The objective of this work is to identify the relationship between stromal collagen density and aggressiveness of mammary carcinoma in cats, according to the peripheral and central tissue distribution by the Picrossirius Red histochemical method. Image.J® and MatLab® software were used for digital image processing. The mean values of kurtosis and entropy attributes were grouped into a control group, and low and high-grade carcinoma groups, analyzed with one-way ANOVA and Bonferronis multiple comparison test (p <0.01). Interpretation of stromal dynamics is important to evaluate both central and peripheral locations. According to entropy, there was a significant increase in the peripheral density in the carcinoma groups in relation to the control group, which can be justified by blood support. The same can be said of the central region, with a significant gain in collagen fibers from the tumors, indicated by kurtosis. The results suggest the presence of increases in stromal density in mammary carcinomas of cats, regardless of their graduation, and occurring in both regions.(AU)
Assuntos
Animais , Gatos , Gatos , Colágeno/análise , Neoplasias Mamárias Animais , Imuno-HistoquímicaRESUMO
Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch's t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC.
Assuntos
Algoritmos , Carcinoma Hepatocelular/classificação , Neoplasias Hepáticas/classificação , Carcinoma Hepatocelular/diagnóstico por imagem , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Tomografia Computadorizada Multidetectores , Curva ROC , Reprodutibilidade dos Testes , Máquina de Vetores de SuporteRESUMO
Abstract Introduction Breast cancer is the first leading cause of death for women in Brazil as well as in most countries in the world. Due to the relation between the breast density and the risk of breast cancer, in medical practice, the breast density classification is merely visual and dependent on professional experience, making this task very subjective. The purpose of this paper is to investigate image features based on histograms and Haralick texture descriptors so as to separate mammographic images into categories of breast density using an Artificial Neural Network. Methods We used 307 mammographic images from the INbreast digital database, extracting histogram features and texture descriptors of all mammograms and selecting them with the K-means technique. Then, these groups of selected features were used as inputs of an Artificial Neural Network to classify the images automatically into the four categories reported by radiologists. Results An average accuracy of 92.9% was obtained in a few tests using only some of the Haralick texture descriptors. Also, the accuracy rate increased to 98.95% when texture descriptors were mixed with some features based on a histogram. Conclusion Texture descriptors have proven to be better than gray levels features at differentiating the breast densities in mammographic images. From this paper, it was possible to automate the feature selection and the classification with acceptable error rates since the extraction of the features is suitable to the characteristics of the images involving the problem.
RESUMO
AIMS: To assess the clinical efficacy of diagnostic procedures for breast cancer at a teaching hospital using internal auditing tools and quality control measures. METHODS: A retrospective assessment of 500 patients who underwent core needle biopsy (wide-bore needle biopsy; WBN) of palpable or non-palpable breast nodes that were submitted for at least one cytological examination (fine needle aspiration (FNA) cytology and/or imprint of a WBN specimen). For statistical analysis the auditing tool and quality control proposed by the National Health Service breast screening programme was utilised. RESULTS: For FNA, full specificity, positive predictive value, inadequate rates and suspicious rates were satisfactory while absolute sensitivity, complete sensitivity, false negatives and false positives were unsatisfactory. For imprint, absolute sensitivity, complete sensitivity, inadequate rate from cancers and suspicious rates were satisfactory, and the remaining indicators were unsatisfactory. WBN displayed the best performance with absolute sensitivity, complete sensitivity, false negative, suspicious rates, full specificity and predictive value showing satisfactory results and only one unsatisfactory result (false positive). CONCLUSIONS: Based on an overall analysis, WBN displayed the highest clinical efficacy compared with FNA and imprint, and demonstrated adequate safety for confirming the appropriate diagnosis and management of patients, ensuring the efficacy of the service.
Assuntos
Biópsia por Agulha , Neoplasias da Mama/diagnóstico , Hospitais de Ensino , Programas de Rastreamento/métodos , Indicadores de Qualidade em Assistência à Saúde , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Biópsia por Agulha Fina , Biópsia por Agulha/normas , Brasil , Neoplasias da Mama/patologia , Reações Falso-Negativas , Reações Falso-Positivas , Feminino , Fidelidade a Diretrizes , Hospitais de Ensino/normas , Humanos , Programas de Rastreamento/normas , Auditoria Médica , Pessoa de Meia-Idade , Guias de Prática Clínica como Assunto , Valor Preditivo dos Testes , Avaliação de Programas e Projetos de Saúde , Controle de Qualidade , Indicadores de Qualidade em Assistência à Saúde/normas , Estudos Retrospectivos , Sensibilidade e Especificidade , Adulto JovemRESUMO
OBJETIVO: Avaliar o impacto sobre o treinamento de residentes utilizando uma ferramenta computacional dedicada à avaliação do desempenho da leitura de imagens radiológicas convencionais e digitais. MATERIAIS E MÉTODOS: O treinamento foi realizado no Laboratório de Qualificação de Imagens Médicas (QualIM). Os residentes de radiologia efetuaram cerca de 1.000 leituras de um total de 60 imagens obtidas de um simulador estatístico (Alvim®) que apresenta fibras e microcalcificações de dimensões variadas. O desempenhodos residentes na detecção dessas estruturas foi avaliado por meio de parâmetros estatísticos. RESULTADOS:Os resultados da probabilidade de detectabilidade foram de 0,789 e 0,818 para os sistemas convencional e digital, respectivamente. As taxas de falso-positivos foram de 8% e 6% e os valores de verdadeiro- -positivos, de 66% e 70%, respectivamente. O valor de kappa total foi 0,553 para as leituras em negatoscópio e 0,615 em monitor. A área sob a curva ROC foi de 0,716 para leitura em filme e 0,810 para monitor.CONCLUSÃO: O treinamento proposto mostrou ser efetivo e apresentou impacto positivo sobre o desempenhodos residentes, constituindo-se em interessante ferramenta pedagógica. Os resultados sugerem que o método de treinamento baseado na leitura de simuladores pode produzir um melhor desempenho dos profissionais na interpretação das imagens mamográficas.
OBJECTIVE: The present study was aimed at evaluating the performance of residents trained in the reading of conventional and digital mammography images with a specific computational tool. MATERIALS AND METHODS: The training was accomplished in the Laboratory of Medical Images Qualification (QualIM û Laboratório de Qualificação de Imagens Médicas). Residents in radiology performed approximately 1,000 readings of a set of 60 images acquired from a statistical phantom (Alvim®) presenting microcalcifications and fibers with different sizes. The analysis of the residents' performance in the detection of these structures was based on statistical parameters. RESULTS: Values for detection probability were respectively 0.789 and 0.818 for conventional and digital systems. False-positive rates were 8% and 6%, and true-positive rates, 66% and 70% respectively. The total kappa value was 0.553 for readings on the negatoscope (hard-copy readings), and 0.615 on the monitor (soft-copy readings). The area under the ROC curve was 0.716 forhard-copy readings and 0.810 for soft-copy readings. CONCLUSION: The training has showed to be effective,with a positive impact on the residents' performance, representing an interesting educational tool. The resultsof the present study suggest that this method of training based on the reading of images from phantoms can improve the practitioners' performance in the interpretation of mammographic images.