Xu Yefu, Zheng Shijie, Tian Qingyi, Kou Zhuoyan, Li Wenqing, Xie Xinhui, Wu Xiaotao
Department of Spine Surgery, ZhongDa Hospital, School of Medicine Southeast University Nanjing China.
JOR Spine. 2024 Sep 17;7(3):e70003. doi: 10.1002/jsp2.70003. eCollection 2024 Sep.
Lumbar disc herniation (LDH) is a prevalent cause of low back pain. LDH patients commonly experience paraspinal muscle atrophy and fatty infiltration (FI), which further exacerbates the symptoms of low back pain. Magnetic resonance imaging (MRI) is crucial for assessing paraspinal muscle condition. Our study aims to develop a dual-model for automated muscle segmentation and FI annotation on MRI, assisting clinicians evaluate LDH conditions comprehensively.
The study retrospectively collected data diagnosed with LDH from December 2020 to May 2022. The dataset was split into a 7:3 ratio for training and testing, with an external test set prepared to validate model generalizability. The model's performance was evaluated using average precision (AP), recall and F1 score. The consistency was assessed using the Dice similarity coefficient (DSC) and Cohen's Kappa. The mean absolute percentage error (MAPE) was calculated to assess the error of the model measurements of relative cross-sectional area (rCSA) and FI. Calculate the MAPE of FI measured by threshold algorithms to compare with the model.
A total of 417 patients being evaluated, comprising 216 males and 201 females, with a mean age of 49 ± 15 years. In the internal test set, the muscle segmentation model achieved an overall DSC of 0.92 ± 0.10, recall of 92.60%, and AP of 0.98. The fat annotation model attained a recall of 91.30%, F1 Score of 0.82, and Cohen's Kappa of 0.76. However, there was a decrease on the external test set. For rCSA measurements, except for longissimus (10.89%), the MAPE of other muscles was less than 10%. When comparing the errors of FI for each paraspinal muscle, the MAPE of the model was lower than that of the threshold algorithm.
The models demonstrate outstanding performance, with lower error in FI measurement compared to thresholding algorithms.
腰椎间盘突出症（LDH）是下腰痛的常见原因。LDH患者通常会出现椎旁肌萎缩和脂肪浸润（FI），这会进一步加重下腰痛症状。磁共振成像（MRI）对于评估椎旁肌状况至关重要。我们的研究旨在开发一种用于MRI上肌肉自动分割和FI标注的双模型，协助临床医生全面评估LDH病情。
本研究回顾性收集了2020年12月至2022年5月诊断为LDH的数据。数据集按7:3的比例分为训练集和测试集，并准备了外部测试集以验证模型的通用性。使用平均精度（AP）、召回率和F1分数评估模型性能。使用骰子相似系数（DSC）和科恩卡方系数评估一致性。计算平均绝对百分比误差（MAPE）以评估模型对相对横截面积（rCSA）和FI测量的误差。计算阈值算法测量FI的MAPE以与模型进行比较。
共评估了417例患者，其中男性216例，女性201例，平均年龄49±15岁。在内测试集中，肌肉分割模型的总体DSC为0.92±0.10，召回率为92.60%，AP为0.98。脂肪标注模型的召回率为91.30%，F1分数为0.82，科恩卡方系数为0.76。然而，外部测试集的性能有所下降。对于rCSA测量，除最长肌外（10.89%），其他肌肉的MAPE均小于10%。比较各椎旁肌FI的误差时，模型的MAPE低于阈值算法。
这些模型表现出色，与阈值算法相比，FI测量误差更低。
