TY - JOUR
T1 - Validation of a new multiple osteochondromas classification through Switching Neural Networks
AU - Mordenti, Marina
AU - Ferrari, Enrico
AU - Pedrini, Elena
AU - Fabbri, Nicola
AU - Campanacci, Laura
AU - Muselli, Marco
AU - Sangiorgi, Luca
PY - 2013/3
Y1 - 2013/3
N2 - 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.
AB - 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.
KW - EXT1/EXT2
KW - Genotype-phenotype correlation
KW - Multiple osteochondromas
KW - Patients classification
KW - Switching neural network
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U2 - 10.1002/ajmg.a.35819
DO - 10.1002/ajmg.a.35819
M3 - Article
C2 - 23401177
AN - SCOPUS:84874210289
VL - 161
SP - 556
EP - 560
JO - American Journal of Medical Genetics, Part A
JF - American Journal of Medical Genetics, Part A
SN - 1552-4825
IS - 3
ER -