BRUSSELS (EGMN) – Analysis of plain x-ray images of knee joints from 60 patients with osteoarthritis confirmed that a novel method for assessing bone trabecular structure adjacent to knee joints provides a reliable prediction of future disease progression.

Assessment of bone trabecular integrity by fractal signature analysis “provides an osteoarthritis imaging biomarker that is a prognostic marker of knee osteoarthritis progression,” Dr. Virginia Byers Kraus said at the annual World Congress on Osteoarthritis.

Baseline bone trabecular integrity predicted roughly 85% of the change in joint space area during 2 years of follow-up in patients with osteoarthritis (OA). The new study, which used x-rays from 60 patients with OA and 67 controls, is the second report to document the prognostic accuracy of fractal signature analysis of bone trabecular integrity in OA patients. The first report, also from Dr. Kraus and her associates, came out last year, and involved 138 OA patients who were followed for 3 years (Arthritis Rheum. 2009;60:3711-22).

“The next step is to compare fractal signature analysis head to head with MRI and look at its ability to predict MRI changes” in OA patients, and “its ability to identify OA in the preradiographic stage,�� Dr. Kraus said in an interview.

Fractal signature analysis of bone trabecular integrity using x-ray images “gives you the ability to more fully phenotype patients than we’ve been able to, and it is less costly than MRI,” said Dr. Kraus, a rheumatologist and professor of medicine at Duke University in Durham, North Carolina. “It’s very promising for identifying patients at high risk for progression in [an intervention] trial, and possibly to screen patients in the clinic.”

Fractal signature analysis evaluates the complexity of detail of a two-dimensional image. Researchers first reported using fractal signature analysis in 1991 to assess bone architecture in radiographs of OA joints. Past studies also successfully used the method to assess osteoporosis and arthritis of the spine, hip, wrists, hands, and knees before and after surgery. Fractal signature analysis has the major advantage of not being very sensitive to image-acquisition quality.

Although fractal signature analysis involves a complex statistical analysis of x-ray image data of bone structure adjacent to a patient’s knee joint, Dr. Kraus and her associates incorporated that analysis into “KneeAnalyzer” software developed by Optasia Medical, a British company. Now that the software exists, “it is easy to use. It’s just a tool to get at bone trabecular integrity. I think it can easily be widely adopted,” she said.

The new study used data collected in a non–therapeutic methods trial sponsored by Pfizer Inc. The data set included 60 women with knee OA, an average age of 58 years, and an average body mass index of 35.6 kg/m². (All participants in this arm of the study had a BMI of at least 30.) The 67 women controls had an average age of 55 years, all had a BMI of 28 or less, and all had no knee symptoms, no radiographic signs of knee OA, and no history of knee fracture, surgery, or disease.

The researchers assessed bone trabecular integrity using fractal signature analysis on radiographs taken at baseline, and after 12 and 24 months. The results showed that baseline measurements in the vertical dimension of bone trabecular integrity predicted changes in joint space area at 12 and 24 months, and in joint space width at 24 months. Baseline measures in the horizontal dimension were not predictive. The predicted changes based on baseline bone trabecular integrity accounted for 85%-87% of the actual change in joint space area over 24 months, Dr. Kraus reported at the congress, which was organized by the Osteoarthritis Research Society International.

Analysis of baseline and follow-up measures in the control subjects allowed the researchers to attribute progression in patients to an OA-specific process and not as the result of aging.

Dr. Kraus said that she had no relevant disclosures. One coauthor is an employee of Optasia Medical, and Optasia provided the software used for the radiograph analyses. Another coauthor is an employee of Pfizer, and Pfizer supplied the database used in the study.

Copyright (c) 2010 Elsevier Global Medical News. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed.

布鲁塞尔(EGMN)——对60例骨关节炎患者膝关节X线片进行的分析表明，一种用于评估膝关节附近骨小梁结构完整性的新方法可对患者未来疾病进展状况做出可靠预测。

 

本研究数据来自一项非治疗性试验，共纳入60例膝盖骨关节炎(OA)女性患者，平均年龄58岁，平均体重指数(BMI) 35.6 kg/m²(所有OA患者BMI均≥30 kg/m²)。对照组共计67例女性，平均年龄55岁，BMI均≤28 kg/m²，无任何膝关节症状且放射显影未发现任何膝关节OA体征，之前也没有膝关节骨折、手术或疾病史。

 

研究者对患者基线、12个月和24个月时的膝关节X线片进行了分形特征分析，结果显示，基线时骨小梁垂直方向的结构完整性可预测12和24个月时关节间隙面积的变化，还可预测24个月时关节间隙宽度的变化。基线时水平方向的测量值无预测能力。分析结果对关节间隙面积在24个月时的实际变化情况的预测准确度高达85%~87%。研究同时测量了对照组受试者基线和随访时的相关指标，以确保骨关节改变是缘于OA疾病进展，而非年龄增长导致的骨关节老化。研究者表示下一步还将采用磁共振成像(MRI)来验证分形特征分析的准确度。

 

本研究利用骨X线图像纹理分形特征分析对骨小梁结构的完整性进行了评估，该方法可对二维X线图像的复杂细节进行评估，在判断骨关节炎预后方面准确度高(2年时关节间隙面积改变预测成功率高达85%)，价格经济(相较于MRI)，且其对图像采集质量差异不敏感，有望作为一种新型骨关节炎影像学标记物用于门诊筛检高危患者。

 

本研究所用放射显影图片分析软件(KneeAnalyzer)和受试者数据库分别由Optasia医药公司和辉瑞公司提供。研究者表示无相关披露。另有2位共同作者分别任职于上述2家公司。

 

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