| Abstract: |
Multiple osteochondromas (MO), previously known as hereditary multiple exostoses (HME), is an autosomal dominant disease characterized by the formation of several benign cartilage-capped bone growth defined osteochondromas or exostoses. Various clinical classifications have been proposed but a consensus has not been reached. The aim of this study was to validate (using a machine learning approach) an "easy to use" tool to characterize MO patients in three classes according to the number of bone segments affected, the presence of skeletal deformities and/or functional limitations. The proposed classification has been validated (with a highly satisfactory mean accuracy) by analyzing 150 different variables on 289 MO patients through a Switching Neural Network approach (a novel classification technique capable of deriving models described by intelligible rules in if-then form). This approach allowed us to identify ankle valgism, Madelung deformity and limitation of the hip extra-rotation as "tags" of the three clinical classes. In conclusion, the proposed classification provides an efficient system to characterize this rare disease and is able to define homogeneous cohorts of patients to investigate MO pathogenesis. © 2013 Wiley Periodicals, Inc. |
