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Abstract Background Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery. Methods We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models). Results The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62. Conclusions Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.

Abstract Background Lumbar disc herniation is one of the leading causes of chronic low back pain. Surgery remains the therapy of choice when conservative approaches fail. Full-endoscopic approaches represent a promising alternative to the well-established microsurgical technique. However, high-grade evidence comparing these techniques is still scarce. Methods Patients presenting with lumbar disc herniation will be included. The intervention group will obtain full-endoscopic disc decompression, whereas the control group will be treated by microsurgical disc decompression. We will apply a comprehensive cohort study design involving a randomized and a prospective non-randomized study arm. Patients who do not consent to be randomized will be assigned to the non-randomized arm. The primary outcome will be the Oswestry Disability Index (ODI). Secondary outcomes involve the visual analog scale (VAS) of pain and the SF-36 health questionnaire. Furthermore, clinical characteristics including duration of hospital stay, operation time, and complications as well as laboratory markers, such as C-reactive protein, white blood cell counts, and interleukin 6 will be determined and compared. Discussion This study will significantly contribute to the current evidence available in the literature by evaluating the outcome of the full-endoscopic technique against the gold standard for lumbar disc herniation in a clinically relevant study setup. Additionally, the study design allows us to include patients not willing to be randomized in a prospective parallel study arm and to evaluate the impact of randomization on outcomes and include. The results could help to improve the future therapy in patients suffering from lumbar disc herniation. Trial registration This study was prospectively registered in The German Clinical Trials Register (DRKS), a German WHO primary registry, under the registration number: DRKS00025786. Registered on July 7, 2021.

Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification of the dural sack cross-sectional area are essential for the evaluation of these conditions. Current manual measurement methods are time-consuming and prone to inter-observer variability. Our study developed and validated deep learning models, specifically U-Net, Attention U-Net, and MultiResUNet, for the automated detection and measurement of the dural sack area in lumbar spine MRI, using a dataset of 515 patients with symptomatic back pain and externally validating the results based on 50 patient scans. The U-Net model achieved an accuracy of 0.9990 and 0.9987 on the initial and external validation datasets, respectively. The Attention U-Net model reported an accuracy of 0.9992 and 0.9989, while the MultiResUNet model displayed a remarkable accuracy of 0.9996 and 0.9995, respectively. All models showed promising precision, recall, and F1-score metrics, along with reduced mean absolute errors compared to the ground truth manual method. In conclusion, our study demonstrates the potential of these deep learning models for the automated detection and measurement of the dural sack cross-sectional area in lumbar spine MRI. The proposed models achieve high-performance metrics in both the initial and external validation datasets, indicating their potential utility as valuable clinical tools for the evaluation of lumbar spine pathologies. Future studies with larger sample sizes and multicenter data are warranted to validate the generalizability of the model further and to explore the potential integration of this approach into routine clinical practice.

Introduction: Minimal-invasive instrumentation techniques have become a workhorse in spine surgery and require constant clinical evaluations. We sought to analyze patient-reported outcome measures (PROMs) and clinicopathological characteristics of thoracolumbar fracture stabilizations utilizing a minimal-invasive percutaneous dorsal screw-rod system. Methods: We included all patients with thoracolumbar spine fractures who underwent minimal-invasive percutaneous spine stabilization in our clinics since inception and who have at least 1 year of follow-up data. Clinical characteristics (length of hospital stay (LOS), operation time (OT), and complications), PROMs (preoperative (pre-op), 3-weeks postoperative (post-op), 1-year postoperative: eq5D, COMI, ODI, NRS back pain), and laboratory markers (leucocytes, c-reactive protein (CRP)) were analyzed, finding significant associations between these study variables and PROMs. Results: A total of 68 patients (m: 45.6%; f: 54.4%; mean age: 76.9 ± 13.9) were included. The most common fracture types according to the AO classification were A3 (40.3%) and A4 (40.3%), followed by B2 (7.46%) and B1 (5.97%). The Median American Society of Anesthesiologists (ASA) score was 3 (range: 1–4). Stabilized levels ranged from TH4 to L5 (mean number of targeted levels: 4.25 ± 1.4), with TH10-L2 (12/68) and TH11-L3 (11/68) being the most frequent site of surgery. Mean OT and LOS were 92.2 ± 28.2 min and 14.3 ± 6.9 days, respectively. We observed 9/68 complications (13.2%), mostly involving screw misalignments and loosening. CRP increased from 24.9 ± 33.3 pre-op to 34.8 ± 29.9 post-op (p < 0.001), whereas leucocyte counts remained stable. All PROMs showed a marked significant improvement for both 3-week and 1-year evaluations compared to the preoperative situation. Interestingly, we did not find an impact of OT, LOS, lab markers, complications, and other clinical characteristics on PROMs. Notably, a higher number of stabilized levels did not affect PROMs. Conclusions: Minimal-invasive stabilization of thoracolumbar fractures utilizing a dorsal percutaneous approach resulted in significant PROM outcome improvements, although we observed a complication rate of 13.2% for up to 1 year of follow-up. PROMs were not significantly associated with clinicopathological characteristics, technique-related variables, or the number of targeted levels.