Research Article Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI Jorge I. Galván-Tejada, 1 José M. Celaya-Padilla, 1 Victor Treviño, 1,2 and José G. Tamez-Peña 2 1 Grupo de Investigaci´ on en Bioinform´ atica, Escuela de Medicina, Tecnol´ ogico de Monterrey, 64849 Monterrey, NL, Mexico 2 Departamento de Investigaci´ on e Innovaci´ on, Escuela de Medicina, Tecnol´ ogico de Monterrey, 64710 Monterrey, NL, Mexico Correspondence should be addressed to Jorge I. Galv´ an-Tejada; gatejo@gmail.com Received 8 May 2015; Revised 29 July 2015; Accepted 4 August 2015 Academic Editor: Lei Chen Copyright © 2015 Jorge I. Galv´ an-Tejada et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. Te pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate diferences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were signifcantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. Te multivariate JSW models signifcantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. 1. Introduction Knee pain is the most common and disabling symptom of Osteoarthritis (OA) [1, 2]. Tis disease afects 1 in every 10 adults over 60 years in the United States and the rate of incidence is incrementing due to changes in lifestyle and life expectancy [3–7]. Te prevalence and the symptomatic importance of pain in OA subjects make pain prediction a very important task for the management of OA patients. Pain is a late manifestation of a pathological change in joint tissues; therefore, the early detection of pathological process may be used to determine who is at risk of developing OA related pain. Tis early detection of the underling pathology may be possible with the aid of noninvasive procedures like medical imaging. Medical imaging has proved to be a very important and efective tool in OA diagnosis; it is also the most common frst-hand information for physicians and a probed form to obtain a good approach to OA staging [8–13]. Due to its maturity, simplicity and broad base deployment of X-Ray, it is the primary medical imaging modality used in OA diagnosis and staging. Radiological OA has been defned as subjects presenting bone alterations (osteophytes) and reduced joint space [14]. Tis fndings have been correlated to joint symptoms of pain and stifness [15]; but the bony changes prognosis power have not been properly studied in longitudinal studies [16–19]. Te biggest challenge facing radiological correlation to symptomatic OA is the multifacto- rial source of joint pain and the subjective perception of pain [20]. Other challenge has been the lack of standardized image assessment procedures that allow a proper evaluation and comparisons of OA studies. To overcome these limitations, validated subject questionnaires [21, 22] and standardized image assessments have been developed [23–26]. Te Osteoarthritis Initiative (OAI) has been recollecting thousands of clinical data in OA patients, subjects at risk, and control subjects using validated questionnaires and Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2015, Article ID 794141, 10 pages http://dx.doi.org/10.1155/2015/794141