When does CT myelography add value beyond MRI for lumbar degenerative disease?

Convolutional Neural Network Incorporating Multiple Attention Mechanisms for MRI Classification of Lumbar Spinal Stenosis

BACKGROUND

Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading to a need for automated methods.

OBJECTIVE

This study aims to develop a convolutional neural network (CNN)-based deep learning model integrated with multiple attention mechanisms to improve the accuracy and robustness of LSS classification via MRI images.

METHODS

The proposed model is trained on a standardized MRI dataset sourced from multiple institutions, encompassing various lumbar degenerative conditions. During preprocessing, techniques such as image normalization and data augmentation are employed to enhance the model's performance. The network incorporates a Multi-Headed Self-Attention Module, a Slot Attention Module, and a Channel and Spatial Attention Module, each contributing to better feature extraction and classification.

RESULTS

The model achieved 95.2% classification accuracy, 94.7% precision, 94.3% recall, and 94.5% F1 score on the validation set. Ablation experiments confirmed the significant impact of the attention mechanisms in improving the model's classification capabilities.

CONCLUSION

The integration of multiple attention mechanisms enhances the model's ability to accurately classify LSS in MRI images, demonstrating its potential as a tool for automated diagnosis. This study paves the way for future research in applying attention mechanisms to the automated diagnosis of lumbar spinal stenosis and other complex spinal conditions.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms

BACKGROUND

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS.

METHODS

A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared.

RESULTS

The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805).

CONCLUSIONS

A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

Multimodal management strategies for chronic pain after spinal surgery: a comprehensive review

"Chronic pain after spinal surgery" (CPSS) is a nonspecific term for cases in which the end result of surgery generally does not meet the preoperative expectations of the patient and surgeon. This term has replaced the previous term i.e., failed back surgery syndrome. CPSS is challenging for both patients and doctors. Despite advancements in surgical techniques and technologies, a subset of patients continue to experience persistent or recurrent pain postoperatively. This review provides an overview of the multimodal management for CPSS, ranging from conservative management to revision surgery. Drawing on recent research and clinical experience, we aimed to offer insights into the diverse strategies available to improve the quality of life of CPSS patients.

Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography

BACKGROUND

The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients.

METHODS

A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not.

RESULTS

The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis.

CONCLUSIONS

ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.

Imaging diagnosis in peripheral nerve injury

Peripheral nerve injuries (PNIs) can be caused by various factors, ranging from penetrating injury to compression, stretch and ischemia, and can result in a range of clinical manifestations. Therapeutic interventions can vary depending on the severity, site, and cause of the injury. Imaging plays a crucial role in the precise orientation and planning of surgical interventions, as well as in monitoring the progression of the injury and evaluating treatment outcomes. PNIs can be categorized based on severity into neurapraxia, axonotmesis, and neurotmesis. While PNIs are more common in upper limbs, the localization of the injured site can be challenging. Currently, a variety of imaging modalities including ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI) and positron emission tomography (PET) have been applied in detection and diagnosis of PNIs, and the imaging efficiency and accuracy many vary based on the nature of injuries and severity. This article provides an overview of the causes, severity, and clinical manifestations of PNIs and highlights the role of imaging in their management.

Failed Back Surgery Syndrome: No Longer a Surgeon's Defeat-A Narrative Review

The introduction of the term Persistent Spinal Pain Syndrome (PSPS-T1/2), replacing the older term Failed Back Surgery Syndrome (FBSS), has significantly influenced our approach to diagnosing and treating post-surgical spinal pain. This comprehensive review discusses this change and its effects on patient care. Various diagnostic methods are employed to elucidate the underlying causes of back pain, and this information is critical in guiding treatment decisions. The management of PSPS-T1/2 involves both causative treatments, which directly address the root cause of pain, and symptomatic treatments, which focus on managing the symptoms of pain and improving overall function. The importance of a multidisciplinary and holistic approach is emphasized in the treatment of PSPS-T1/2. This approach is patient-centered and treatment plans are customized to individual patient needs and circumstances. The review concludes with a reflection on the impact of the new PSPS nomenclature on the perception and management of post-surgical spinal pain.