New MR-based measures for the evaluation of age-related lumbar paraspinal muscle degeneration

Deep-Learning-Based Radiomics to Predict Surgical Risk Factors for Lumbar Disc Herniation in Young Patients: A Multicenter Study

Objective

The aim of this study is to develop and validate a deep-learning radiomics model for predicting surgical risk factors for lumbar disc herniation (LDH) in young patients to assist clinicians in identifying surgical candidates, alleviating symptoms, and improving prognosis.

Methods

A retrospective analysis of patients from two medical centers was conducted. From sagittal and axial MR images, the regions of interest were handcrafted to extract radiomics features. Various machine-learning algorithms were employed and combined with clinical features, resulting in the development of a deep-learning radiomics nomogram (DLRN) to predict surgical risk factors for LDH in young adults. The efficacy of the different models and the clinical benefits of the model were compared.

Results

We derived six sets of features, including clinical features, radiomics features (Rad_SAG and Rad_AXI) and deep learning features (DL_SAG and DL_AXI) from sagittal and axial MR images, as well as fused deep-learning radiomics (DLR) features. The support vector machine(SVM) algorithm exhibited the best performance. The area under the curve (AUC) of DLR in the training and testing cohorts of 0.991 and 0.939, respectively, were significantly better than those of the models developed with radiomics(Rad_SAG=0.914 and 0.863, Rad_AXI=0.927 and 0.85) and deep-learning features(DL_SAG=0.959 and 0.818, DL_AXI=0.960 and 0.811). The AUC of DLRN coupled with clinical features(ODI, Pfirrmann grade, SLRT, MMFI, and MSU classification) were 0.994 and 0.941 in the training and testing cohorts, respectively. Analysis of the calibration and decision curves demonstrated good agreement between the predicted and observed outcomes, and the use of the DLRN to predict the need for surgical treatment of LDH demonstrated significant clinical benefits.

Conclusion

The DLRN established based on clinical and DLR features effectively predicts surgical risk factors for LDH in young adults, offering valuable insights for diagnosis and treatment.

Age- and sex-dependent differences in the morphology and composition of paraspinal muscles between subjects with and without lumbar degenerative diseases

BACKGROUND

The quality of the paraspinal muscles has been recommended as a surrogate marker for the evaluation of the severity of the lumbar degenerative diseases (LDD). The purpose of this study is to determine the age- and sex-dependent differences in the morphology and composition of the paraspinal muscles between LDD and asymptomatic subjects.

METHODS

We analyzed data from 370 patients and 327 asymptomatic volunteers aged between 18-85 years. The measurement of the cross-sectional area (CSA) of the erector spinae, multifidus, and psoas at the L4/5-disc level was performed by the magnetic resonance imaging (MRI). The fatty infiltration ratio (FI %) of the multifidus and erector spinae was calculated.

RESULTS

FI % of the lumbar paraspinal muscles were significantly and positively correlated with the severity of LDD instead of the CSA. Males had greater CSA than females, and females showed higher FI % than males in the paraspinal muscles. With the increase of age, the CSA of the lumbar paraspinal muscles gradually decreased, and the psoas showed the most significant decreasing trend. However, the FI % gradually increased in both LDD and asymptomatic groups with aging.

CONCLUSION

Age- and sex-dependent differences were found in the morphology and composition of the paraspinal muscles between subjects with and without LDD. Further long-term follow up investigations and basic studies will continue to confirm the natural history of the paraspinal muscles with aging and their association with LDD.