Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
Sci Rep. 2017 Aug 23;7(1):9177. doi: 10.1038/s41598-017-09013-7.
Economic costs of osteoarthritis (OA) are considerable. However, there are no clinical tools to predict the progression of OA or guide patients to a correct treatment for preventing OA. We tested the ability of our cartilage degeneration algorithm to predict the subject-specific development of OA and separate groups with different OA levels. The algorithm was able to predict OA progression similarly with the experimental follow-up data and separate subjects with radiographical OA (Kellgren-Lawrence (KL) grade 2 and 3) from healthy subjects (KL0). Maximum degeneration and degenerated volumes within cartilage were significantly higher (p < 0.05) in OA compared to healthy subjects, KL3 group showing the highest degeneration values. Presented algorithm shows a great potential to predict subject-specific progression of knee OA and has a clinical potential by simulating the effect of interventions on the progression of OA, thus helping decision making in an attempt to delay or prevent further OA symptoms.
骨关节炎（OA）的经济成本相当可观。然而，目前还没有临床工具可以预测 OA 的进展，也无法指导患者进行正确的治疗以预防 OA。我们测试了我们的软骨退变算法预测 OA 进展和区分不同 OA 水平患者的能力。该算法能够根据实验随访数据和影像学 OA（Kellgren-Lawrence（KL）分级 2 和 3）患者与健康受试者（KL0）来预测 OA 进展，效果类似。OA 患者的软骨最大退变和退变体积明显高于健康受试者（p < 0.05），KL3 组的退变值最高。该算法具有预测膝关节 OA 特定患者进展的巨大潜力，并具有临床潜力，可以模拟干预对 OA 进展的影响，从而帮助决策，试图延缓或预防进一步的 OA 症状。
