(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level. (2) Methods: After data preprocessing, the modified 3D Deeplab V3+ network of the deep learning model was used for the automatic segmentation of multiple structures from MR images at the L4/5 level. We performed five-fold cross-validation to evaluate the performance of the deep learning model. Subsequently, the Dice Similarity Coefficient (DSC), precision, and recall were also used to assess the deep learning model’s performance. Pearson’s correlation coefficient analysis and the Wilcoxon signed-rank test were employed to compare the morphometric measurements of 3D reconstruction models generated by manual and automatic segmentation. (3) Results: The deep learning model obtained an overall average DSC of 0.886, an average precision of 0.899, and an average recall of 0.881 on the test sets. Furthermore, all morphometry-related measurements of 3D reconstruction models revealed no significant difference between ground truth and automatic segmentation. Strong linear relationships and correlations were also obtained in the morphometry-related measurements of 3D reconstruction models between ground truth and automated segmentation. (4) Conclusions: We found it feasible to perform automated segmentation of multiple structures from MR images, which would facilitate lumbar surgical evaluation by establishing 3D reconstruction models at the L4/5 level.

Objective: Creating a 3D lumbar model and planning a personalized puncture trajectory has an advantage in establishing the working channel for percutaneous endoscopic lumbar discectomy (PELD). However, existing 3D lumbar models, which seldom include lumbar nerves and dural sac reconstructions, primarily depend on CT images for preoperative trajectory planning. Therefore, our study aims to further investigate the relationship between different virtual working channels and the 3D lumbar model, which includes automated MR image segmentation of lumbar bone, nerves, and dural sac at the L4/L5 level.Methods: Preoperative lumbar MR images of 50 patients with L4/L5 lumbar disc herniation were collected from a teaching hospital between March 2020 and July 2020. Automated MR image segmentation was initially used to create a 3D model of the lumbar spine, including the L4 vertebrae, L5 vertebrae, intervertebral disc, L4 nerves, dural sac, and skin. Thirty were then randomly chosen from the segmentation results to clarify the relationship between various virtual working channels and the lumbar 3D model. A bivariate Spearman's rank correlation analysis was used in this study.Results: Preoperative MR images of 50 patients (34 males, mean age 45.6 ± 6 years) were used to train and validate the automated segmentation model, which had mean Dice scores of 0.906, 0.891, 0.896, 0.695, 0.892, and 0.892 for the L4 vertebrae, L5 vertebrae, intervertebral disc, L4 nerves, dural sac, and skin, respectively. With an increase in the coronal plane angle (CPA), there was a reduction in the intersection volume involving the L4 nerves and atypical structures. Conversely, the intersection volume encompassing the dural sac, L4 inferior articular process, and L5 superior articular process increased; the total intersection volume showed a fluctuating pattern: it initially decreased, followed by an increase, and then decreased once more. As the cross‐section angle (CSA) increased, there was a rise in the intersection volume of both the L4 nerves and the dural sac; the intersection volume involving the L4 inferior articular process grew while that of the L5 superior articular process diminished; the overall intersection volume and the intersection volume of atypical structures initially decreased, followed by an increase.Conclusion: In terms of regularity, the optimal angles for L4/L5 PELD are a CSA of 15° and a CPA of 15°–20°, minimizing harm to the vertebral bones, facet joint, spinal nerves, and dural sac. Additionally, our 3D preoperative planning method could enhance puncture trajectories for individual patients, potentially advancing surgical navigation, robots, and artificial intelligence in PELD procedures.

Study Design.: Retrospective cohort study. Objective.: To develop machine learning (ML) models integrating clinical/imaging variables for predicting 3- and 6-month outcomes of radiofrequency (RF) treatment in lumbar facetogenic pain, and an independent temporal validation cohort was used to evaluate the model’s performance. Shapley Additive Explanations (SHAP) analysis was utilized to identify key variables and construct a simplified model. Summary of Background Data.: Early identification of RF-responsive patients remains challenging, with limited non-invasive prognostic tools available. Methods.: Six ML models were trained using 16 clinical/imaging variables from 372 RF-treated patients. Model performance was evaluated via AUROC, with SHAP analysis identifying key variables. Simplified models using clinical-only, imaging-only, and SHAP-selected variables were compared. Results.: In the discovery (n=312) and temporal validation (n=60) cohorts, 141 and 26 patients had unsuccessful 3-month outcomes, respectively. The logistic model outperformed others, achieving AUROCs of 0.834 (95% CI: 0.725-0.942) and 0.818 (0.713-0.923) for 3-month prediction in discovery and validation cohorts. Simplified models showed comparable performance (discovery AUROC: 0.795-0.837; validation: 0.699-0.814). Six-month predictions demonstrated similar robustness (discovery AUROC: 0.813; validation: 0.783). Decision curve analysis confirmed the logistic model’s clinical utility, providing net benefits at threshold probabilities >40%. Conclusions.: The Logistic model, which is based on clinical and imaging variables, has the potential to facilitate early screening of patients who might benefit from RF treatment in the short- and long-term. SHAP analysis helps evaluate the impact of variables and build simplified models with comparable performance. The key variables identified in this study require further verification through external geographic validations. Level of Evidence.: 3.

Background/Objectives: Lumbar disc herniation (LDH) is the most common condition associated with low back pain, and it adversely impacts individuals’ health. The interplay between energy metabolism and apoptosis is critical, as the loss of viable cells in the intervertebral disc (IVD) can lead to a cascade of degenerative changes. Efferocytosis is a key biological process that maintains homeostasis by removing apoptotic cells, resolving inflammation, and promoting tissue repair. Therefore, enhancing mitochondrial energy metabolism and efferocytosis function in IVD cells holds great promise as a potential therapeutic approach for LDH. Methods: In this study, energy metabolism and efferocytosis-related differentially expressed genes (EMERDEGs) were identified from the transcriptomic datasets of LDH. Machine learning approaches were used to identify key genes. Functional enrichment analyses were performed to elucidate the biological roles of these genes. The functions of the hub genes were validated by RT-qPCR. The CIBERSORT algorithm was used to compare immune infiltration between LDH and Control groups. Additionally, we used single-cell RNA sequencing dataset to analyze cell-specific expression of the hub genes. Results: By using bioinformatics methods, we identified six EMERDEGs hub genes (IL6R, TNF, MAPK13, ELANE, PLAUR, ABCA1) and verified them using RT-qPCR. Functional enrichment analysis revealed that these genes were primarily associated with inflammatory response, chemokine production, and cellular energy metabolism. Further, we identified candidate drugs as potential treatments for LDH. Additionally, in immune infiltration analysis, the abundance of activated dendritic cells, neutrophils, and gamma delta T cells varied significantly between the LDH group and Control group. The scRNA-seq analysis showed that these hub genes were mainly expressed in chondrocyte-like cells. Conclusions: The identified EMERDEG hub genes and pathways offer novel insights into the molecular mechanisms underlying LDH and suggest potential therapeutic targets.

Background: Lumbar disc herniation (LDH) is the most common etiological cause of low back pain (LBP). Objective and precise pain evaluation is of significant clinical value. Functional near-infrared spectroscopy (fNIRS) as a noninvasive neuroimaging modality, has been increasingly validated to reflect subjective pain perception through hemodynamic correlates. This study aimed to analyze the fNIRS changes in patients with LDH about to receive Unilateral Biportal Endoscopy and to further explore the feasibility of fNIRS as an objective biomarkers for clinical assessment of LDH. Methods: Resting-state fNIRS data were acquired from 67 preoperative LDH patients and 20 healthy controls (HC). Brain functional maps—including z-standardized fractional amplitude of low-frequency fluctuations (zfALFF) and seed-based functional connectivity (FC)—were extracted and quantified. Group-level comparisons were performed between LDH and HC groups across four predefined regions of interest; additionally, correlation analyses were conducted between fNIRS metrics and clinical assessment scores within the LDH cohort. Results: Compared with HC, LDH patients exhibited significantly altered zfALFF in the medial prefrontal cortex (mPFC): decreased amplitude at channel CH12 (t = −2.031, p = 0.045) and increased amplitude at CH21 (t = 2.462, p = 0.016). Whole-brain FC analysis further revealed widespread changes—particularly between the parietal somatosensory cortex and prefrontal regions. Among all tested FC–clinical indicator associations, 56 reached statistical significance after FDR correction (q < 0.05). VAS_ lumbar and SF-36_SF exhibited the highest number of significant connections. Conclusions: LDH patients with LBP exhibit notable alterations in prefrontal resting-state ALFF and FC between the parietal somatosensory cortex and prefrontal cortex relative to HC. Importantly, these neural alterations exhibit significant associations with both pain severity (VAS) and long-term health-related quality of life (SF-36), thereby strengthening their candidacy as neural correlates meriting prospective validation as objective, mechanism-informed biomarkers for clinical evaluation of lumbar disc herniation (LDH). Moreover, these findings highlight candidate neural targets for future longitudinal studies investigating early prognostic prediction and treatment response monitoring in LDH.