■本研究旨在评估基于机器学习的影像组学工具在使用下肢肌肉磁共振成像(MRI)检查区分肢带肌营养不良R2(LGMDR2)和免疫介导的坏死性肌病(IMNM)之间的可行性。
■机构审查委员会批准后,30例经基因证实的LGMDR2患者(12名女性;年龄,34.0±11.3)和45例IMNM患者(28例女性;年龄,49.2±16.6)在2014年7月至2022年8月期间接受了下肢MRI检查,包括T1加权和交互式分解水和脂肪,回声不对称和最小二乘估计(IDEAL)序列。获得了肌肉的影像组学特征,并采用四种机器学习算法选择最佳的影像组学分类器进行鉴别诊断。执行此选定的算法以构建T1加权(TM),仅限水(WM),或仅小腿的组合模型(CM),仅大腿,或者小腿和大腿MR图像,分别。并使用曲线下面积(AUC)研究了它们的诊断性能,并将其与由两名专门从事肌肉骨骼成像的放射科医生对小腿和大腿肌肉进行评分的改良Mercuri量表构建的半定量模型进行了比较。
■逻辑回归(LR)模型是最佳的影像组学模型。除TM外,仅大腿图像(AUC0.893,0.913)的WM和CM的性能优于仅小腿图像(AUC0.846,0.880)。对于“小腿+大腿”图像,TM,WM,和CM模型总是表现最好(AUC0.953,0.907,0.953),具有出色的准确性(92.0,84.0,88.0%)。小牛Mercuri模型的AUC,大腿,“小腿+大腿”图像分别为0.847、0.900和0.953,准确率为84.0、84.0、88.0%。
■基于机器学习的影像组学模型可以区分LGMDR2和IMNM,表现优于视觉评估。通过结合小腿和大腿图像建立的模型具有出色的诊断效率。
UNASSIGNED: This study aimed to assess the feasibility of a machine learning-based radiomics tools to discriminate between Limb-girdle muscular dystrophy R2 (LGMDR2) and immune-mediated necrotizing myopathy (IMNM) using lower-limb muscle magnetic resonance imaging (MRI) examination.
UNASSIGNED: After institutional review board approval, 30 patients with genetically proven LGMDR2 (12 females; age, 34.0 ± 11.3) and 45 patients with IMNM (28 females; age, 49.2 ± 16.6) who underwent lower-limb MRI examination including T1-weighted and interactive decomposition water and fat with echos asymmetric and least-squares estimation (IDEAL) sequences between July 2014 and August 2022 were included. Radiomics features of muscles were obtained, and four machine learning algorithms were conducted to select the optimal radiomics classifier for differential diagnosis. This selected algorithm was performed to construct the T1-weighted (TM), water-only (WM), or the combined model (CM) for calf-only, thigh-only, or the calf and thigh MR images, respectively. And their diagnostic performance was studied using area under the curve (AUC) and compared to the semi-quantitative model constructed by the modified Mercuri scale of calf and thigh muscles scored by two radiologists specialized in musculoskeletal imaging.
UNASSIGNED: The logistic regression (LR) model was the optimal radiomics model. The performance of the WM and CM for thigh-only images (AUC 0.893, 0.913) was better than those for calf-only images (AUC 0.846, 0.880) except the TM. For \"calf + thigh\" images, the TM, WM, and CM models always performed best (AUC 0.953, 0.907, 0.953) with excellent accuracy (92.0, 84.0, 88.0%). The AUCs of the Mercuri model of the calf, thigh, and \"calf + thigh\" images were 0.847, 0.900, and 0.953 with accuracy (84.0, 84.0, 88.0%).
UNASSIGNED: Machine learning-based radiomics models can differentiate LGMDR2 from IMNM, performing better than visual assessment. The model built by combining calf and thigh images presents excellent diagnostic efficiency.