Desarrollo de un biomarcador basado en el análisis de variables estadísticas para el diagnóstico de la miocardiopatía hipertrófica a partir del análisis de texturas en imágenes de resonancia magnética

[ES] Dada la elevada prevalencia de las enfermedades cardiovasculares en la actualidad, se hace cada vez más necesario el desarrollo de sistemas de ayuda a la decisión que proporcionen una información adicional al médico con el fin de poder diagnosticarlas adecuadamente y realizar el tratamiento más apropiado. En este caso, se pretende desarrollar un biomarcador para el diagnóstico de miocardiopatía hipertrófica. La miocardiopatía hipertrófica (MCH) es una enfermedad del músculo del corazón que se caracteriza por el aumento del grosor de sus paredes, que no se deba a una causa externa al músculo (por ejemplo, hipertensión, valvulopatías, etc.). Se estima que la miocardiopatía hipertrófica afecta a 1 de cada 500 personas. No puede atribuirse a una causa evidente, pero es hereditaria en un alto porcentaje de casos. Existen varías patologías que provocan el engrosamiento del músculo cardíaco, por lo que un biomarcador que permita identificar la miocardiopatía hipertrófica podría ser de gran utilidad. Con el análisis de texturas a partir de imágenes médicas se pretende llegar a distinguir entre pacientes con miocardiopatía hipertrófica y sanos, sin tener que recurrir a pruebas invasivas en el paciente. Para investigar la capacidad del análisis de texturas para diferenciar pacientes con MCH, se ha realizado un estudio retrospectivo que incluirá pacientes que sufren MCH y el mismo número de pacientes sanos. Se empleará una técnica de segmentación semiautomática que proporciona más fiabilidad mediante el uso del software Segment. El miocardio del ventrículo izquierdo se segmentará de acuerdo con el modelo de 17 segmentos a partir de secuencias de cine de RM de imágenes en eje corto. Una vez obtenida la segmentación, se realizará un análisis de texturas del miocardio y se obtendrán variables estadísticas con las que se evaluará la posibilidad de distinguir MCH y pacientes sanos. Para la clasificación se empleará un clasificador basado en técnicas de aprendizaje máquina mediante el uso diferentes combinaciones de características de textura para obtener un modelo que proporcione una clasificación óptima.[EN] Given the high prevalence of cardiovascular diseases, it is becoming necessary to develop decision support systems which are able to provide additional information to the doctor, in order to be able to diagnose them adequately and perform the most appropriate treatment. In this case, we aim to develop a biomarker for the diagnosis of hypertrophic cardiomyopathy. Hypertrophic cardiomyopathy (HCM) is a disease of the heart muscle that is characterized by an increase in the thickness of its walls, which is not caused by any external element of the muscle (for example, hypertension, valvular heart disease, etc.). It is estimated that hypertrophic cardiomyopathy affects 1 in 500 people. It cannot be attributed to an obvious cause, but it is hereditary in a high percentage of cases. There are several pathologies that cause thickening of the heart muscle, so a biomarker that allows identification of hypertrophic cardiomyopathy could be very useful. With the analysis of textures from medical images we aim to distinguish between patients with hypertrophic and healthy cardiomyopathy, without having to perform invasive testing in the patient. To investigate the ability of texture analysis to differentiate patients with HCM, a retrospective study has been conducted. It will include patients suffering from HCM and the same number of healthy patients. A semi-automatic segmentation technique will be used that provides more reliability through the use of Segment software. The myocardium of the left ventricle will be segmented according to the 17-segment model from short-axis MR imaging sequences. Once the segmentation is obtained, an analysis of myocardial textures will be performed and statistical variables will be obtained with which the possibility of distinguishing HCM and healthy patients will be evaluated. For classification, a classifier based on machine learning techniques will be used by using different combinations of texture characteristics to obtain a model that provides an optimal classification.Piñeiro Vidal, T. (2019). Desarrollo de un biomarcador basado en el análisis de variables estadísticas para el diagnóstico de la miocardiopatía hipertrófica a partir del análisis de texturas en imágenes de resonancia magnética. http://hdl.handle.net/10251/124588TFG

Determination of Image-based Biomarkers for the Diagnosis of Hypertrophic Cardiomyopathy, Hypertensive Cardiomyopathy and Amyloidosis From Texture Analysis in Cardiac MRI

[EN] Hypertrophic cardiomyopathy (HCM), hypertensive cardiomyopathy (HIP), and amyloidosis (AM) are pathologies in which a thickening of a portion of the myocardium occurs. All of them are manifested in a similar way on magnetic resonance images, which means that in most cases it is necessary to resort to the use of invasive diagnostic techniques. The objective of this work is to develop quantitative biomarkers that can differentiate between patients with these three pathologies using texture analysis on cardiac magnetic resonance imaging (MRI). In this study, a total of 103 patients underwent cine MRI. Two studies were carried out, one binary with patients with HCM and HIP and one multiclass considering the three pathologies. The left ventricular myocardium was segmented according to the standardized 17-segment model. A total of 43 features for each of the six segments were extracted using 5 different statistical methods. Four predictive models were implemented to evaluate the performance of the classification. Good precision results were obtained in both studies. For the binary study, a maximum AUC of 0.91 +/- 0.06 was obtained with the K-Nearest Neighbours model and for the multiclass study the best performance (AUC = 0.89 +/- 0.12) was achieved using the Support Vector Machine classifier.DM acknowledges financial support from the Conselleria d'Educació, Investigació, Cultura i Esport, Generalitat Valenciana (grants AEST/2019/037 and AEST/2020/029), from the Agencia Valenciana de la Innovación, Generalitat Valenciana (ref. INNCAD00/19/085), and from the Centro para el Desarrollo Tecnológico Industrial (Programa Eurostars-2, actuación Interempresas Internacional), Spanish Ministerio de Ciencia, Innovación y Universidades (ref. CIIP20192020).Vidal Sospedra, I.; Ruiz-España, S.; Piñeiro-Vidal, T.; Santabárbara, J.; Maceira, A.; Moratal, D. (2020). Determination of Image-based Biomarkers for the Diagnosis of Hypertrophic Cardiomyopathy, Hypertensive Cardiomyopathy and Amyloidosis From Texture Analysis in Cardiac MRI. IEEE Computer Society. 230-235. https://doi.org/10.1109/BIBE50027.2020.00045S23023

Multiclass texture-based PI-RADS classification in multiparametric MRI: performance evaluation of the DWI sequence

Jaen-Lorites, JM.; Ruiz-España, S.; Piñeiro-Vidal, T.; Canto Serrano, ID.; Santabárbara, J.; Moratal, D. (2020). Multiclass texture-based PI-RADS classification in multiparametric MRI: performance evaluation of the DWI sequence. Springer Nature. 207-207. http://hdl.handle.net/10251/179211S20720