Analysis of the clinical and radiological outcomes of percutaneous cervical nucleoplasty: A case-control study

Minimally Invasive Percutaneous Techniques for the Treatment of Cervical Disc Herniation: A Systematic Review and Meta-Analysis

Background: In recent decades, the adoption of minimally invasive (non-endoscopic) cervical techniques has grown significantly. Advancements in surgical instrumentation have broadened the spectrum of available percutaneous interventions, thus providing viable alternative treatment options for patients with prolonged, conservative treatment-resistant ailments due to contained cervical disc herniation. The aim of this study was to perform a systematic review and meta-analysis in order to evaluate the effectiveness and safety of minimally invasive percutaneous (non-endoscopic) cervical techniques. Methods: A comprehensive literature search was conducted using the PubMed, Cochrane Library, and SCOPUS databases up to July 2024, in accordance with the PRISMA guidelines. Outcomes measured included Visual Analogue Scale (VAS) scores, the Neck Disability Index (NDI), and MacNab scores, assessing pain relief and functional recovery. The risk of bias was evaluated using the Cochrane risk of bias tool (RoB 2) and the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool, with statistical analyses conducted in R software (version 4.3.1). Results: Out of 847 records, 21 studies (covering 1580 patients) were included in the final analysis. Five different percutaneous minimally invasive cervical procedures were incorporated into this review: nucleoplasty (n = 973), discectomy (n = 311), a combination of nucleoplasty and discectomy (n = 98), annuloplasty (n = 33), and pulsed radiofrequency (n = 17). The mean patient age was 49.5, with a gender distribution of 47.7% male and 52.3% female. A meta-analysis of six studies on cervical nucleoplasty (400 patients) demonstrated a significant reduction in pain scores, with a standardized mean difference (SMD) of -4.68 (95% CI: -8.77; -0.59, p = 0.032). However, a high heterogeneity (I2 = 98.8%, Q = 407.31, p < 0.001) was observed, indicating significant variability across studies. The reoperation rate among patients was 3.4%, with discitis and device-related complications being the most frequently reported adverse events. Conclusions: Minimally invasive percutaneous cervical interventions provide effective pain relief and functional improvement for patients with cervical disc herniation, as evidenced by reductions in VAS scores and positive MacNab outcomes. The choice of the most appropriate technique should be based on individual clinical scenarios, surgeon expertise, and patient preferences, as no single method demonstrates clear superiority according to clinical outcomes or complication rates.

Use of Navigable Ablation Decompression Treatment (L-DISQ) for Contained Cervical Disc Herniation - Technical Note and Literature Review

Introduction

A new navigable percutaneous disc decompressor (L'DISQ-C, U&I Co., Uijeongbu, Korea), introduced in 2012, is designed to allow direct access to herniated disc material. The L'DISQ device can be curved by rotating a control wheel, directed into disc herniation treats, and decompresses contained herniated discs with minimal collateral thermal damage. This study reports the case of contained central disc herniation in a 34-year-old male with a 2-year follow-up successfully treated with navigable ablation decompression treatment (L-DISQ).

Case Report

A 34-year-old man presented to the outpatient department with a 6-month history of neck pain and bilateral upper limb radiation. His neck pain had increased progressively. At the time of presentation, his neck pain visual analog scale score was 7/10, and his neck disability index score was 30. The magnetic resonance images showed a single fluid-containing lesion with a hyperintense zone at the C4-5 levels with central disc herniation. The patient was successfully treated with the navigable ablation decompression treatment (L-DISQ) procedure.

Conclusion

The navigable ablation decompression treatment (L-DISQ) is a valuable technique in treating contained cervical disc herniation with rapid pain relief and improvements in functional outcomes without any significant injury to surrounding structures. It is safe, precise, and effective in the treatment of symptomatic cervical disc herniations. Large, randomized, and multicenter trials are needed to explore the potential of the same technique further.

Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease

BACKGROUND

Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)-based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD.

METHODS

The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences.

RESULTS

Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix.

CONCLUSIONS

We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making.