TY - JOUR

T1 - Diagnosis of osteoarthritis and prognosis of tibial cartilage loss by quantification of tibia trabecular bone from MRI

AU - Marques, Joselene

AU - Genant, Harry K.

AU - Lillholm, Martin

AU - Dam, Erik Bjørnager

N1 - Copyright © 2012 Wiley Periodicals, Inc.

PY - 2013

Y1 - 2013

N2 - A longitudinal study was used to investigate the quantification of osteoarthritis and prediction of tibial cartilage loss by analysis of the tibia trabecular bone from magnetic resonance images of knees. The Kellgren Lawrence (KL) grades were determined by radiologists and the levels of cartilage loss were assessed by a segmentation process. Aiming to quantify and potentially capture the structure of the trabecular bone anatomy, a machine learning approach used a set of texture features for training a classifier to recognize the trabecular bone of a knee with radiographic osteoarthritis. Using cross-validation, the bone structure marker was used to estimate for each knee both the probability of having radiographic osteoarthritis (KL >1) and the probability of rapid cartilage volume loss. The diagnostic ability reached a median area under the receiver-operator-characteristics curve of 0.92 (P <0.0001), and the prognosis had odds ratio of 3.9 (95% confidence interval: 2.4-6.5). The medians of cartilage loss of the subjects classified as slow and rapid progressors were 1.1% and 4.9% per year, respectively. A preliminary radiological reading of the high and low risk knees put forward an hypothesis of which pathologies the bone marker could be capturing to define the prognosis of cartilage loss. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.

AB - A longitudinal study was used to investigate the quantification of osteoarthritis and prediction of tibial cartilage loss by analysis of the tibia trabecular bone from magnetic resonance images of knees. The Kellgren Lawrence (KL) grades were determined by radiologists and the levels of cartilage loss were assessed by a segmentation process. Aiming to quantify and potentially capture the structure of the trabecular bone anatomy, a machine learning approach used a set of texture features for training a classifier to recognize the trabecular bone of a knee with radiographic osteoarthritis. Using cross-validation, the bone structure marker was used to estimate for each knee both the probability of having radiographic osteoarthritis (KL >1) and the probability of rapid cartilage volume loss. The diagnostic ability reached a median area under the receiver-operator-characteristics curve of 0.92 (P <0.0001), and the prognosis had odds ratio of 3.9 (95% confidence interval: 2.4-6.5). The medians of cartilage loss of the subjects classified as slow and rapid progressors were 1.1% and 4.9% per year, respectively. A preliminary radiological reading of the high and low risk knees put forward an hypothesis of which pathologies the bone marker could be capturing to define the prognosis of cartilage loss. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.

U2 - 10.1002/mrm.24477

DO - 10.1002/mrm.24477

M3 - Journal article

C2 - 22941674

VL - 70

SP - 568

EP - 575

JO - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

SN - 0740-3194

IS - 2

ER -