DTI AS A BIOMARKER TO PREDICT PROGRESSION IN EARLY OA

J.G. Raya , A. Duarte , R. Kijowski , S. Krasnokutsky-Samuels , J. Samuels , A. Ruiz
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Diffusion tensor imaging (DTI) was introduced as a biomarker specific for PG content (MD) and collagen structure (FA) and has demonstrated to be a promising biomarker to diagnose OA.</p></div><div><h3>OBJECTIVE</h3><p>To validate DTI and T<sub>2</sub> of articular cartilage at 3T as biomarkers for OA progression in a population at early stages of the disease and high likelihood of short-term progression.</p></div><div><h3>METHODS</h3><p><u>Study design</u>. We recruited 60 subjects (m/f=23/37, age=61±8 y, BMl=30.7±6.4 kg/cm<sup>2</sup>) with unilateral knee OA (symptomatic with KL≥2) and incipient OA in the contralateral knee (KL=1, no history of injury). We focused on the KL=1 knees since they were at an early stage of disease and had high likelihood of progression according to the OAI dataset. At baseline all subjects underwent a clinical assessment, provided x-rays of the bilateral knees, and MRI of the KL=1 knee. Forty subjects returned for 3-years follow-up evaluation, which included clinical assessment and x-rays of the bilateral knees to capture clinical endpoint for progression. Medial and lateral JSW was measured at each time point in the KL=1 knees. JSN was calculated in the lateral and medial compartments. A JSN &gt;0.7 mm was considered progression.</p><p><u>MRI.</u> The 3T protocol included a radial imaging spin-echo diffusion (RAISED) sequence for DTI measure (TE/TR=35/1500 ms, 105 spokes/image, 6 directions, b-values=0, 300 s/mm<sup>2</sup>, resolution 0.6 × 0.6 × 3 mm<sup>3</sup>), and a multi echo T<sub>2</sub>-weighted sequence for T<sub>2</sub> calculation (TE=10.5 to 126 ms, train length 12, echo train 10.5 ms, TR=4.3s, resolution 0.6 × 0.6 × 3 mm<sup>3</sup>). Diffusion-weighted images were reconstructed using a non-linear motion correction. Cartilage regions (TrF, LF, MF, MT, LT, and P) were segmented. T<sub>2</sub> and DTI parameter maps of mean diffusivity (MD) and fractional anisotropy (FA) were calculated averaged over every region.</p><p><u>Statistics.</u> Pearson or Spearman coefficients were used to evaluate the association between baseline MRI parameters and biological variables (age, sex, and BMI), and 3-year radiographic progression (JSN, change in KL). Partial correlations were performed to correct for biological variables. Group differences between progressors and non-progressors were assessed using either a two-sided unpaired t-test or Wilcoxon test depending on normality of the data (K-S test). Finally, a stepwise forward algorithm was used including both biological variables and [DTI and T<sub>2</sub>] to identify the optimal logistic regression model predicting 3-y progression in JSN. 10-fold cross-validation was used to validate the logistic regression models.</p></div><div><h3>RESULTS</h3><p><u>Radio</u>g<u>raphic pro</u>g<u>ression.</u> 12 subjects had a JSN of &gt; 0.7 mm (mean: 1.5±0.7mm, range 0.9 to 2.9 mm), with 10 of the 12 showing that JSN progression in the medial compartment. JSN did not correlate with biological variables.</p><p><u>Prediction of pro</u>g<u>ression</u>. Baseline MD values in the MF and MT were significantly increased in progressors compared to non-progressors (+15.4% and +6.0% respectively, p&lt;0.01) and had a positive correlation with the 3-year JSN (r=-0.36, p&lt;0.05). Baseline FA was marginally lower in the MT of progressors compared to non-progressors (-10.0%, p=0.06). No differences could be established for T<sub>2</sub> in any region. Stepwise model algorithm rejected biological variables as predictors of progression (p&gt;0.26). Baseline MF MD was the strongest predictor of progression (p&lt;0.05) and showed an odds-ratio per one-standard deviation (ORsD, SD=0.10 × 10<sup>-3</sup> mm<sup>2</sup>/s) of 6.5 with a 95% confidence interval (95%-CI) [1.3, 33.0]. An optimal threshold for baseline MF MD of 1.63 × 10<sup>-3</sup> mm<sup>2</sup>/s had accuracy=79.4%, sensitivity=63.3% and specificity=87.0% to predict progression 3 years after.</p></div><div><h3>CONCLUSION</h3><p>This is the first study that validates DTI as a biomarker for OA progression. Since OA progression is more frequent on the medial compartment is natural that MD in the medial compartment is better associated with JSN. The large odds-ratio associated with MD and the high accuracy make this a strong candidate for progression. 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Abstract

INTRODUCTION

Assessment of disease progression in patients with incipient OA remains a major challenge. Quantitative imaging biomarkers have the advantage of being sensitive to changes in cartilage composition, which represent an early feature of the disease. Several MRI parameters have shown sensitive to PG content, but only T2 being partially sensitive to collagen. Diffusion tensor imaging (DTI) was introduced as a biomarker specific for PG content (MD) and collagen structure (FA) and has demonstrated to be a promising biomarker to diagnose OA.

OBJECTIVE

To validate DTI and T2 of articular cartilage at 3T as biomarkers for OA progression in a population at early stages of the disease and high likelihood of short-term progression.

METHODS

Study design. We recruited 60 subjects (m/f=23/37, age=61±8 y, BMl=30.7±6.4 kg/cm2) with unilateral knee OA (symptomatic with KL≥2) and incipient OA in the contralateral knee (KL=1, no history of injury). We focused on the KL=1 knees since they were at an early stage of disease and had high likelihood of progression according to the OAI dataset. At baseline all subjects underwent a clinical assessment, provided x-rays of the bilateral knees, and MRI of the KL=1 knee. Forty subjects returned for 3-years follow-up evaluation, which included clinical assessment and x-rays of the bilateral knees to capture clinical endpoint for progression. Medial and lateral JSW was measured at each time point in the KL=1 knees. JSN was calculated in the lateral and medial compartments. A JSN >0.7 mm was considered progression.

MRI. The 3T protocol included a radial imaging spin-echo diffusion (RAISED) sequence for DTI measure (TE/TR=35/1500 ms, 105 spokes/image, 6 directions, b-values=0, 300 s/mm2, resolution 0.6 × 0.6 × 3 mm3), and a multi echo T2-weighted sequence for T2 calculation (TE=10.5 to 126 ms, train length 12, echo train 10.5 ms, TR=4.3s, resolution 0.6 × 0.6 × 3 mm3). Diffusion-weighted images were reconstructed using a non-linear motion correction. Cartilage regions (TrF, LF, MF, MT, LT, and P) were segmented. T2 and DTI parameter maps of mean diffusivity (MD) and fractional anisotropy (FA) were calculated averaged over every region.

Statistics. Pearson or Spearman coefficients were used to evaluate the association between baseline MRI parameters and biological variables (age, sex, and BMI), and 3-year radiographic progression (JSN, change in KL). Partial correlations were performed to correct for biological variables. Group differences between progressors and non-progressors were assessed using either a two-sided unpaired t-test or Wilcoxon test depending on normality of the data (K-S test). Finally, a stepwise forward algorithm was used including both biological variables and [DTI and T2] to identify the optimal logistic regression model predicting 3-y progression in JSN. 10-fold cross-validation was used to validate the logistic regression models.

RESULTS

Radiographic progression. 12 subjects had a JSN of > 0.7 mm (mean: 1.5±0.7mm, range 0.9 to 2.9 mm), with 10 of the 12 showing that JSN progression in the medial compartment. JSN did not correlate with biological variables.

Prediction of progression. Baseline MD values in the MF and MT were significantly increased in progressors compared to non-progressors (+15.4% and +6.0% respectively, p<0.01) and had a positive correlation with the 3-year JSN (r=-0.36, p<0.05). Baseline FA was marginally lower in the MT of progressors compared to non-progressors (-10.0%, p=0.06). No differences could be established for T2 in any region. Stepwise model algorithm rejected biological variables as predictors of progression (p>0.26). Baseline MF MD was the strongest predictor of progression (p<0.05) and showed an odds-ratio per one-standard deviation (ORsD, SD=0.10 × 10-3 mm2/s) of 6.5 with a 95% confidence interval (95%-CI) [1.3, 33.0]. An optimal threshold for baseline MF MD of 1.63 × 10-3 mm2/s had accuracy=79.4%, sensitivity=63.3% and specificity=87.0% to predict progression 3 years after.

CONCLUSION

This is the first study that validates DTI as a biomarker for OA progression. Since OA progression is more frequent on the medial compartment is natural that MD in the medial compartment is better associated with JSN. The large odds-ratio associated with MD and the high accuracy make this a strong candidate for progression. In summary, DTI has potential for prognosis of 3-year radiographic changes in a population with early stages of disease.

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作为预测早期 OA 进展的生物标志物的 DTI
引言 评估初发 OA 患者的疾病进展仍是一项重大挑战。定量成像生物标志物的优点是对软骨成分的变化敏感,而软骨成分的变化是疾病的早期特征。一些核磁共振成像参数显示对 PG 含量敏感,但只有 T2 对胶原蛋白部分敏感。弥散张量成像(DTI)被认为是PG含量(MD)和胶原结构(FA)的特异性生物标志物,并已被证明是诊断OA的一种有前途的生物标志物。目的验证3T下关节软骨的DTI和T2作为OA进展的生物标志物是否适用于处于疾病早期阶段且极有可能发生短期进展的人群。我们招募了 60 名受试者(男/女=23/37,年龄=61±8 y,BMl=30.7±6.4 kg/cm2),他们患有单侧膝关节 OA(KL≥2,有症状)和对侧膝关节的初期 OA(KL=1,无受伤史)。我们重点研究了KL=1的膝关节,因为根据OAI数据集,这些膝关节处于疾病的早期阶段,病情恶化的可能性很大。在基线阶段,所有受试者都接受了临床评估,提供了双侧膝关节的X光片和KL=1膝关节的核磁共振成像。40 名受试者接受了为期 3 年的随访评估,其中包括临床评估和双侧膝关节的 X 光片,以捕捉病情发展的临床终点。在每个时间点测量 KL=1 膝关节的内侧和外侧 JSW。计算外侧和内侧的 JSN。JSN达到0.7毫米即为进展。3T方案包括用于DTI测量的径向成像自旋回波扩散(RAISED)序列(TE/TR=35/1500 ms,105辐条/图像,6个方向,b值=0,300 s/mm2,分辨率0.6 × 0.6 × 3 mm3),以及用于T2计算的多回波T2加权序列(TE=10.5至126 ms,序列长度12,回波序列10.5 ms,TR=4.3s,分辨率0.6 × 0.6 × 3 mm3)。使用非线性运动校正重建弥散加权图像。对软骨区域(TrF、LF、MF、MT、LT 和 P)进行分割。计算每个区域的平均 T2 和 DTI 参数图,包括平均扩散率 (MD) 和分数各向异性 (FA)。皮尔逊系数或斯皮尔曼系数用于评估基线 MRI 参数与生物变量(年龄、性别和体重指数)和 3 年放射学进展(JSN、KL 变化)之间的关联。对生物变量进行了部分相关性校正。根据数据的正态性(K-S 检验),采用双侧非配对 t 检验或 Wilcoxon 检验评估进展者和非进展者之间的组间差异。最后,采用包括生物变量和[DTI和T2]的逐步向前算法来确定预测JSN 3年进展的最佳逻辑回归模型。结果放射学进展。12名受试者的JSN为> 0.7毫米(平均值:1.5±0.7毫米,范围为0.9至2.9毫米),其中10名受试者的JSN进展位于内侧区。JSN与生物变量无相关性。MF和MT的基线MD值在进展者中明显高于非进展者(分别为+15.4%和+6.0%,p<0.01),并且与3年的JSN呈正相关(r=-0.36,p<0.05)。与非进展者相比,进展者 MT 的基线 FA 略低(-10.0%,p=0.06)。任何区域的 T2 均无差异。逐步模型算法拒绝将生物变量作为进展的预测因素(p>0.26)。基线 MF MD 是病情进展的最强预测因子(p<0.05),每一个标准差(ORsD,SD=0.10 × 10-3 mm2/s)的几率为 6.5,95% 置信区间(95%-CI)为 [1.3, 33.0]。基线 MF MD 的最佳阈值为 1.63 × 10-3 mm2/s,预测 3 年后病情进展的准确性=79.4%,灵敏度=63.3%,特异性=87.0%。由于内侧区的 OA 进展更为频繁,因此内侧区的 MD 与 JSN 的相关性更高。与 MD 相关的几率比较大,且准确性较高,因此是判断病情进展的有力候选指标。总之,DTI 有潜力预测疾病早期人群 3 年的影像学变化。
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Osteoarthritis imaging
Osteoarthritis imaging Radiology and Imaging
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