RESUMO
Abstract Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it's proposed a CADx system that quantify the bilateral similarity of the patient's knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it's proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years' follow-up.
Resumen La osteoartritis (OA) es el tipo de artritis más común. OA es una enfermedad limitante que afecta a 1 de 10 adultos con 60 años o más. Las imágenes de rayos-x son una técnica de diagnóstico primario que permite conocer el estado de OA, las cuales el experto lee y cuantifica así la etapa de la enfermedad. El Diagnóstico Asistido por Computadora (CADx, por sus siglas en inglés) ha buscado automatizar el diagnóstico de OA para ayudar al radiólogo en la detección y control; sin embargo, el dolor provocado por la progresión de la enfermedad es dejado atrás. En este trabajo se propone un sistema de CADx que cuantifica la similitud bilateral de las rodillas de los pacientes, con el fin de correlacionar el grado de asimetría con el dolor. Inicialmente, las imágenes de las rodillas fueron alineadas usando el algoritmo B-spline para su registro, después, un conjunto de métricas estándar fue cuantificado; finalmente, con estas métricas se propone un modelo multivariado para predecir el dolor de rodilla desarrollado en 48 meses. La metodología fue validada con 131 pacientes obtenidos de la base de datos de la Osteoarthritis Initiative (OAI). Los resultados sugieren que las métricas pueden ser asociadas con los puntajes de KellgrenLawrence; además, los modelos predicen significativamente el dolor crónico de rodilla con: AUC 0.756, 0.704 y 0.7113, al inicio, un año y dos años después, respectivamente.
RESUMO
Abstract: Knee pain is the most common and disabling symptom in Osteoarthritis (OA). Joint pain is a late manifestation of the OA. In earlier stages of the disease changes in joint structures are shown. Also, formation of bony osteophytes, cartilage degradation, and joint space reduction which are some of the most common, among others. The main goal of this study is to associate radiological features with the joint pain symptom. Univariate and multivariate studies were performed using Bioinformatics tools to determine the relationship of future pain with early radiological evidence of the disease. All data was retrieved from the Osteoarthritis Initiative repository (OAI). A case-control study was done using available data from participants in OAI database. Radiological data was assessed with different OAI radiology groups. We have used quantitative and semi-quantitative scores to measure two different relations between radiological data in three different time points. The goal was to track the appearance and prevalence of pain as a symptom. All predictive models were statistically significant (P ≤ 0,05), obtaining the receiving operating characteristic (ROC) curves with their respective area under the curves (AUC) of 0.6516, 0.6174, and 0.6737 for T-0, T-1 and T-2 in quantitative analysis. For semi-quantitative an AU C of 0.6865, 0.6486, and 0.6406 for T-0, T-1 and T-2. The models obtained in the Bioinformatics study suggest that early joint structure changes can be associated with future joint pain. An image-based biomarker that could predict future pain, measured in early OA stages, could become a useful tool to improve the quality of life of people dealing OA.
Resumen: El dolor de rodilla es el síntoma más común y limitante de la Osteoartritis (OA), además de presentarse como una manifestación tardía de la enfermedad. Los cambios que ocurren en las estructuras de las articulaciones se presentan en las primeras etapas de la OA. Algunos de los cambios más comunes son la formación de osteofitos óseos, degradación del cartílago, y la reducción del espacio en la articulación, entre otros. El principal objetivo de este estudio es la asociación de características radiológicas con el síntoma de dolor de las articulaciones, para lo que fueron realizados dos estudios: univariado y multivariado, usando herramientas bioinformáticas para determinar la relación de futuro dolor con la evidencia radiológica temprana de la enfermedad. Todos los datos fueron recuperados de la Osteoarthritis Initiative repository (OAI). Este estudio de caso-control se llevó a cabo utilizando los datos disponibles de los participantes de la base de datos de la OAI. Los datos radiológicos fueron evaluados con diferentes grupos de radiología de la OAI. Fueron usadas puntuaciones cuantitativas y semi- cuantitativas para medir las dos diferentes relaciones entre los datos radiológicos en tres diferentes puntos de tiempo. El objetivo fue seguir la trayectoria de la aparición y prevalencia del dolor como síntoma. Todos los modelos predictivos fueron estadísticamente significativos (P ≤ 0,05). Para el análisis cuantitativo se calcularon las áreas bajo la curva (AUC): 0.6516, 0.6174, y 0.6737 para T-0, T-1 y T-2, y para el análisis semi-cuantitativo se calcularon las AU C: 0.6865, 0.6486, y 0.6406 para T-0, T-1 y T-2. Los modelos obtenidos en el estudio bioinformático sugieren que los cambios tempranos en la estructura de las articulaciones pueden estar asociados con el futuro dolor de rodilla. Un biomarcador basado en imágenes que pueda predecir el futuro dolor, medido en las primeras etapas de OA, podría convertirse en una herramienta útil para mejorar la calidad de vida de la gente que padece OA.
RESUMO
Preeclampsia (PE) is a pregnancy complex disease, distinguished by high blood pressure and proteinuria, diagnosed after the 20th gestation week. Depending on the values of blood pressure, urine protein concentrations, symptomatology, and onset of disease there is a wide range of phenotypes, from mild forms developing predominantly at the end of pregnancy to severe forms developing in the early stage of pregnancy. In the worst cases severe forms of PE could lead to systemic endothelial dysfunction, eclampsia, and maternal and/or fetal death. Worldwide the fetal morbidity and mortality related to PE is calculated to be around 8% of the total pregnancies. PE still being an enigma regarding its etiology and pathophysiology, in general a deficient trophoblast invasion during placentation at first stage of pregnancy, in combination with maternal conditions are accepted as a cause of endothelial dysfunction, inflammatory alterations and appearance of symptoms. Depending on the PE multifactorial origin, several in vitro, in vivo, and in silico models have been used to evaluate the PE pathophysiology as well as to identify or test biomarkers predicting, diagnosing or prognosing the syndrome. This review focuses on the most common models used for the study of PE, including those related to placental development, abnormal trophoblast invasion, uteroplacental ischemia, angiogenesis, oxygen deregulation, and immune response to maternal-fetal interactions. The advances in mathematical and computational modeling of metabolic network behavior, gene prioritization, the protein-protein interaction network, the genetics of PE, and the PE prediction/classification are discussed. Finally, the potential of these models to enable understanding of PE pathogenesis and to evaluate new preventative and therapeutic approaches in the management of PE are also highlighted. Impact statement This review is important to the field of preeclampsia (PE), because it provides a description of the principal in vitro, in vivo, and in silico models developed for the study of its principal aspects, and to test emerging therapies or biomarkers predicting the syndrome before their evaluation in clinical trials. Despite the current advance, the field still lacking of new methods and original modeling approaches that leads to new knowledge about pathophysiology. The part of in silico models described in this review has not been considered in the previous reports.
