Uncovering associations between data-driven learned qMRI biomarkers and chronic pain.
Uncovering associations between data-driven learned qMRI biomarkers and chronic pain.
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揭示数据驱动的学习qMRI生物标志物与慢性疼痛之间的关联
DOI：
10.1038/s41598-021-01111-x
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发表时间：
2021-11-09
影响因子：
4.6
通讯作者：
Pedoia V
中科院分区：
文献类型：
--
作者：
Morales AG;Lee JJ;Caliva F;Iriondo C;Liu F;Majumdar S;Pedoia V
膝关节疼痛是膝关节骨关节炎（OA）最常见和最令人衰弱的症状。虽然OA成像生物标志物和疼痛之间存在感知关联，但这种关系存在薄弱或相互矛盾的发现。这项研究使用深度学习（DL）模型来阐明骨骼形状，软骨厚度和从磁共振图像（MRI）中提取的T2弛豫时间与慢性膝关节疼痛之间的关联。应用于训练的慢性疼痛DL模型的类激活图（Grad-CAM）用于评估与疼痛存在和不存在相关的特征的位置。对于软骨厚度生物标志物，对疼痛存在敏感的特征的存在通常位于内侧，而对疼痛不存在特异性的特征通常位于前外侧。这表明软骨厚度和疼痛的相关性各不相同，需要更个性化的平均策略。我们提出了一种新的DL指导的定义，软骨厚度的空间平均的基础上梯度CAM的权重。通过纳入新的生物标志物定义，我们显示了慢性膝关节疼痛建模的显著改善：与软骨厚度室平均值相比，股骨和胫骨DL引导软骨厚度平均值的似然比检验p值分别为7.01 × 10-33和1.93 × 10-14。
Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations between bone shape, cartilage thickness and T2 relaxation times extracted from Magnetic Resonance Images (MRI) and chronic knee pain. Class Activation Maps (Grad-CAM) applied on the trained chronic pain DL models are used to evaluate the locations of features associated with presence and absence of pain. For the cartilage thickness biomarker, the presence of features sensitive for pain presence were generally located in the medial side, while the features specific for pain absence were generally located in the anterior lateral side. This suggests that the association of cartilage thickness and pain varies, requiring a more personalized averaging strategy. We propose a novel DL-guided definition for cartilage thickness spatial averaging based on Grad-CAM weights. We showed a significant improvement modeling chronic knee pain with the inclusion of the novel biomarker definition: likelihood ratio test p-values of 7.01 × 10–33 and 1.93 × 10–14 for DL-guided cartilage thickness averaging for the femur and tibia, respectively, compared to the cartilage thickness compartment averaging.
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影响因子：
4.4
作者：
Lee H;Tajmir S;Lee J;Zissen M;Yeshiwas BA;Alkasab TK;Choy G;Do S
通讯作者：
Do S
影响因子：
4.7
作者：
Baum, Thomas;Joseph, Gabby B.;Arulanandan, Ahilan;Nardo, Lorenzo;Virayavanich, Warapat;Carballido-Gamio, Julio;Nevitt, Michael C.;Lynch, John;McCulloch, Charles E.;Link, Thomas M.
通讯作者：
Link, Thomas M.
影响因子：
27.4
作者：
Bowes MA;Kacena K;Alabas OA;Brett AD;Dube B;Bodick N;Conaghan PG
通讯作者：
Conaghan PG
影响因子：
2.3
作者：
Bedson, John;Croft, Peter R.
通讯作者：
Croft, Peter R.
DOI：
10.1109/34.121791
发表时间：
1992-02-01
影响因子：
23.6
作者：
BESL, PJ;MCKAY, ND
通讯作者：
MCKAY, ND