  Objective is to assess the ability of texture features for detecting
radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view
radiographs. We used lateral view knee radiographs from MOST public use
datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically
detected using landmark detection tool (BoneFinder). Hand-crafted features,
based on LocalBinary Patterns (LBP), were then extracted to describe the
patellar texture. First, a machine learning model (Gradient Boosting Machine)
was trained to detect radiographic PFOA from the LBP features. Furthermore, we
used end-to-end trained deep convolutional neural networks (CNNs) directly on
the texture patches for detecting the PFOA. The proposed classification models
were eventually compared with more conventional reference models that use
clinical assessments and participant characteristics such as age, sex, body
mass index(BMI), the total WOMAC score, and tibiofemoral Kellgren-Lawrence (KL)
grade. Atlas-guided visual assessment of PFOA status by expert readers provided
in the MOST public use datasets was used as a classification outcome for the
models. Performance of prediction models was assessed using the area under the
receiver operating characteristic curve (ROC AUC), the area under the
precision-recall (PR) curve-average precision (AP)-, and Brier score in the
stratified 5-fold cross validation setting.Of the 5507 knees, 953 (17.3%) had
PFOA. AUC and AP for the strongest reference model including age, sex, BMI,
WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487,
respectively. Textural ROI classification using CNN significantly improved the
prediction performance (ROC AUC= 0.889, AP= 0.714). We present the first study
that analyses patellar bone texture for diagnosing PFOA. Our results
demonstrates the potential of using texture features of patella to predict
PFOA.
