McSweeney Terence, Tiulpin Aleksei, Kowlagi Narasimharao, Määttä Juhani, Karppinen Jaro, Saarakkala Simo
Research Unit of Health Sciences and Technology, University of Oulu, Finland.
Neurocenter Oulu, Oulu University Hospital, Oulu, Finland.
Spine (Phila Pa 1976). 2025 Jun 20. doi: 10.1097/BRS.0000000000005435.
A retrospective analysis.
The aim of this study was to identify a robust radiomic signature from deep learning segmentations for intervertebral disc (IVD) degeneration classification.
Low back pain (LBP) is the most common musculoskeletal symptom worldwide and IVD degeneration is an important contributing factor. To improve the quantitative phenotyping of IVD degeneration from T2-weighted magnetic resonance imaging (MRI) and better understand its relationship with LBP, multiple shape and intensity features have been investigated. IVD radiomics have been less studied but could reveal sub-visual imaging characteristics of IVD degeneration.
We used data from Northern Finland Birth Cohort 1966 members who underwent lumbar spine T2-weighted MRI scans at age 45-47 (n=1397). We used a deep learning model to segment the lumbar spine IVDs and extracted 737 radiomic features, as well as calculating IVD height index and peak signal intensity difference. Intraclass correlation coefficients across image and mask perturbations were calculated to identify robust features. Sparse partial least squares discriminant analysis was used to train a Pfirrmann grade classification model.
The radiomics model had balanced accuracy of 76.7% (73.1-80.3%) and Cohen's Kappa of 0.70 (0.67-0.74), compared to 66.0% (62.0-69.9%) and 0.55 (0.51-0.59) for an IVD height index and peak signal intensity model. 2D sphericity and interquartile range emerged as radiomics-based features that were robust and highly correlated to Pfirrmann grade (Spearman's correlation coefficients of -0.72 and -0.77 respectively).
Based on our findings these radiomic signatures could serve as alternatives to the conventional indices, representing a significant advance in the automated quantitative phenotyping of IVD degeneration from standard-of-care MRI.
回顾性分析。
本研究旨在从深度学习分割中识别出一种强大的放射组学特征，用于椎间盘（IVD）退变分类。
腰痛（LBP）是全球最常见的肌肉骨骼症状，IVD退变是一个重要的促成因素。为了从T2加权磁共振成像（MRI）中改善IVD退变的定量表型分析，并更好地理解其与LBP的关系，已经研究了多种形状和强度特征。IVD放射组学的研究较少，但可能揭示IVD退变的亚视觉成像特征。
我们使用了1966年芬兰北部出生队列中45 - 47岁接受腰椎T2加权MRI扫描的成员的数据（n = 1397）。我们使用深度学习模型分割腰椎IVD，并提取737个放射组学特征，同时计算IVD高度指数和峰值信号强度差。计算图像和掩码扰动的组内相关系数以识别稳健特征。使用稀疏偏最小二乘判别分析训练Pfirrmann分级分类模型。
放射组学模型的平衡准确率为76.7%（73.1 - 80.3%），Cohen's Kappa为0.70（0.67 - 0.74），而IVD高度指数和峰值信号强度模型分别为66.0%（62.0 - 69.9%）和0.55（0.51 - 0.59）。二维球形度和四分位间距作为基于放射组学的特征出现，它们稳健且与Pfirrmann分级高度相关（斯皮尔曼相关系数分别为 - 0.72和 - 0.77）。
基于我们的研究结果，这些放射组学特征可作为传统指标的替代方法，代表了从标准护理MRI自动定量分析IVD退变方面的重大进展。
