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arXiv 1902.04880physics.med-phcs.LG
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使用机器学习进行骨纹理分析以预测放射摄影髋关节骨关节炎事件：来自髋关节和膝关节队列（CHECK）研究的数据

Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study

University of Oulu（奥卢大学）；University Medical Center Utrecht（乌得勒支大学医学中心）；University of Zabol（扎博勒大学）；Erasmus University Medical Center（伊拉斯谟大学医学中心）；Delft University of Technology（代尔夫特理工大学）；University of Birjand（比尔詹德大学）

Jukka Hirvasniemi, Willem Paul Gielis, Saeed Arbabi, Rintje Agricola, Willem Evert van Spil, Vahid Arbabi, Harrie Weinans

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摘要：

我们的目的是评估近端股骨和髋臼基于X线片的骨纹理参数预测10年内发生X线片髋关节骨关节炎（rHOA）的能力。使用股骨近端和髋臼的分形特征分析，对基线（987例髋关节）时来自髋关节队列（髋关节队列和膝关节队列）的骨盆X线片进行骨纹理分析。使用弹性网络（机器学习）预测10年后rHOA（Kelling-Lawrence分级（KL）> 1或全髋关节置换术（THR））、关节间隙狭窄评分（JSN，范围0-3）和骨赘评分（OST，范围0-3）的发生率。使用受试者工作特征曲线下面积（ROC AUC）评估预测模型的性能。在基线时无rHOA的987例髋关节中，435例（44%）在10年随访时有rHOA。在基线时JSN等级为0的667个髋关节中，471个（71%）在10年随访时JSN等级> 0。在基线时OST等级为0的613个髋关节中，526个（86%）在10年随访时OST等级> 0。包括年龄、性别和体重指数在内的模型预测rHOA、JSN和OST事件的AUC分别为0.59、0.54和0.51。在模型中纳入骨纹理参数改善了对事件rHOA（当基线KL也包括在模型中时，ROC AUC为0.66和0.71）和JSN（ROC AUC为0.62）的预测，但对事件OST（ROC AUC为0.53）没有改善。骨骼纹理分析为预测10年内发生的rHOA或THR提供了额外信息。

英文摘要：

Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK (Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bone texture using fractal signature analysis in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (Kellgren-Lawrence grade (KL) > 1 or total hip replacement (THR)), joint space narrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade > 0 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade > 0 at 10-year follow-up. AUCs for the models including age, gender, and body mass index to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture parameters in the models improved the prediction of incident rHOA (ROC AUC 0.66 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.53). Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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