A comparison of two methods to evaluate a narrow spinal canal: routine magnetic resonance imaging versus three-dimensional reconstruction

Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level

(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.

Japanese Orthopaedic Association (JOA) clinical practice guidelines on the management of lumbar spinal stenosis, 2021 - Secondary publication

BACKGROUND

The Japanese Orthopaedic Association (JOA) guideline for the management of lumbar spinal stenosis (LSS) was first published in 2011. Since then, the medical care system for LSS has changed and many new articles regarding the epidemiology and diagnostics of LSS, conservative treatments such as new pharmacotherapy and physical therapy, and surgical treatments including minimally invasive surgery have been published. In addition, various issues need to be examined, such as verification of patient-reported outcome measures, and the economic effect of revised medical management of patients with lumbar spinal disorders. Accordingly, in 2019 the JOA clinical guidelines committee decided to update the guideline and consequently established a formulation committee. The purpose of this study was to describe the formulation we implemented for the revision of the guideline, incorporating the recent advances of evidence-based medicine.

METHODS

The JOA LSS guideline formulation committee revised the previous guideline based on the method for preparing clinical guidelines in Japan proposed by the Medical Information Network Distribution Service in 2017. Background and clinical questions were determined followed by a literature search related to each question. Appropriate articles based on keywords were selected from all the searched literature. Using prepared structured abstracts, systematic reviews and meta-analyses were performed. The strength of evidence and recommendations for each clinical question was decided by the committee members.

RESULTS

Eight background and 15 clinical questions were determined. Answers and explanations were described for the background questions. For each clinical question, the strength of evidence and the recommendation were both decided, and an explanation was provided.

CONCLUSIONS

The 2021 clinical practice guideline for the management of LSS was completed according to the latest evidence-based medicine. We expect that this guideline will be useful for all medical providers as an index in daily medical care, as well as for patients with LSS.

Degenerative Lumbar Spine Disease: Imaging and Biomechanics

Chronic low back pain (CLBP) is one of the most common diagnoses encountered when considering years lived with disability. The degenerative changes of the lumbar spine include a wide spectrum of morphological modifications visible on imaging, some of them often asymptomatic or not consistent with symptoms. Phenotyping by considering both clinical and imaging biomarkers can improve the management of CLBP. Depending on the clinical presentation, imaging helps determine the most likely anatomical nociceptive source, thereby enhancing the therapeutic approach by targeting a specific lesion. Three pathologic conditions with an approach based on our experience can be described: (1) pure painful syndromes related to single nociceptive sources (e.g., disk pain, active disk pain, and facet joint osteoarthritis pain), (2) multifactorial painful syndromes, representing a combination of several nociceptive sources (such as lumbar spinal stenosis pain, foraminal stenosis pain, and instability pain), and (3) nonspecific CLBP, often explained by postural (muscular) syndromes.

Dural sac cross-sectional area is a highly effective parameter for spinal anesthesia in geriatric patients undergoing transurethral resection of the prostate: a prospective, double blinded, randomized study

Background

Spinal anesthesia is optimal choice for transurethral resection of the prostate (TURP), but the sensory block should not cross the T10 level. With advancing age, the sensory blockade level increases after spinal injection in some patients with spinal canal stenosis. We optimize the dose of spinal anesthesia according to the decreased ratio of the dural sac cross-sectional area (DSCSA), the purpose of this study is to hypothesis that if DSCSA is an effective parameter to modify the dosage of spinal anesthetics to achieve a T10 blockade in geriatric patients undergoing TURP.

Methods

Sixty geriatric patients schedule for TURP surgery were enrolled in this study. All subjects were randomized divided into two groups, the ultrasound (group U) and the control (group C) groups, patient receive either a dose of 2 ml of 0.5% isobaric bupivacaine in group C, or a modified dose of 0.5% isobaric bupivacaine in group U. We measured the sagittal anteroposterior diameter (D) of the dural sac at the L3–4 level with ultrasound, and calculated the approximate DSCSA (A) according to the following formula: A = π(D/2)², ( π = 3.14). The modified dosage of bupivacaine was adjusted according to the decreased ratio of the DSCSA.

Results

The cephalad spread of the sensory blockade level was significantly lower (P < 0.001) in group U (T10, range T7–T12) compared with group C (T3, range T2–T9). The dosage of bupivacaine was significantly decreased in group U compared with group C (P < 0.001). The regression times of the two segments were delay in group U compared with group C (P < 0.001). The maximal decrease in MAP was significantly higher in the group C than in group U after spinal injection (P < 0.001), without any modifications HR in either group. Eight patients in group C and two patients in group U required ephedrine (P = 0.038).

Conclusions

The DSCSA is a highly effective parameter for spinal anesthesia in geriatric patients undergoing TURP, a modified dose of local anesthetic is a critical factor for controlling the sensory level.

Trial registration

This study was registered in the Chinese Clinical Trial Registry (Registration number: ChiCTR1800015566).on 8, April, 2018.

Dural sac area is a more sensitive parameter for evaluating lumbar spinal stenosis than spinal canal area: A retrospective study

Narrowing of the dural sac cross-sectional area (DSCSA) and spinal canal cross-sectional area (SCCSA) have been considered major causes of lumbar central canal spinal stenosis (LCCSS). DSCSA and SCCSA were previously correlated with subjective walking distance before claudication occurs, aging, and disc degeneration. DSCSA and SCCSA have been ideal morphological parameters for evaluating LCCSS. However, the comparative value of these parameters is unknown and no studies have evaluated the clinical optimal cut-off values of DSCSA and SCCSA. This study assessed which parameter is more sensitive.Both DSCSA and SCCSA samples were collected from 135 patients with LCCSS, and from 130 control subjects who underwent lumbar magnetic resonance imaging (MRI) as part of a medical examination. Axial T2-weighted MRI scans were acquired at the level of facet joint from each subject. DSCSA and SCCSA were measured at the L4-L5 intervertebral level on MRI using a picture archiving and communications system.The average DSCSA value was 151.67 ± 53.59 mm in the control group and 80.04 ± 35.36 mm in the LCCSS group. The corresponding average SCCSA values were 199.95 ± 60.96 and 119.17 ± 49.41 mm. LCCSS patients had significantly lower DSCSA and SCCSA (both P < .001). Regarding the validity of both DSCSA and SCCSA as predictors of LCCSS, Receiver operating characteristic curve analysis revealed an optimal cut-off value for DSCSA of 111.09 mm, with 80.0% sensitivity, 80.8% specificity, and an area under the curve (AUC) of 0.87 (95% confidence interval, 0.83-0.92). The best cut off-point of SCCSA was 147.12 mm, with 74.8% sensitivity, 78.5% specificity, and AUC of 0.85 (95% confidence interval, 0.81-0.89).DSCSA and SCCSA were both significantly associated with LCCSS, with DSCSA being a more sensitive measurement parameter. Thus, to evaluate LCCSS patients, pain specialists should more carefully investigate the DSCSA than SCCSA.