State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.

Musculoskeletal Dimension and Brightness Reference Values in Lumbar Magnetic Resonance Imaging-A Radio-Anatomic Investigation in 80 Healthy Adult Individuals

Background/Objectives: Magnetic resonance imaging (MRI) is the preferred diagnostic means to visualize spinal pathologies, and offers the possibility of precise structural tissue analysis. However, knowledge about MRI-based measurements of physiological cross-sectional musculoskeletal dimensions and associated tissue-specific average structural brightness in the lumbar spine of healthy young women and men is scarce. The current study was planned to investigate characteristic intersexual differences and to provide MRI-related musculoskeletal baseline values before the onset of biological aging. Methods: At a single medical center, lumbar MRI scans of 40 women and 40 men aged 20-40 years who presented with moderate nonspecific low back pain were retrospectively evaluated for sex-specific differences in cross-sectional sizes of the fifth lumbar vertebrae, psoas and posterior paravertebral muscles, and respective sex- and age-dependent average brightness alterations on T2-weighted axial sections in the L5-level. Results: In women (mean age 33.5 years ± 5.0 (standard deviation)), the investigated musculoskeletal cross-sectional area sizes were significantly smaller (p < 0.001) compared to those in men (mean age 33.0 years ± 5.7). Respective average musculoskeletal brightness values were higher in women compared to those in men, and most pronounced in posterior paravertebral muscles (p < 0.001). By correlating brightness results to those of subcutaneous fat tissue, all intersexual differences, including those between fifth lumbar vertebrae and psoas muscles, turned out to be statistically significant. This phenomenon was least pronounced in psoas muscles. Conclusions: Lumbar musculoskeletal parameters showed significantly larger dimensions of investigated anatomical structures in men compared to those in women aged 20-40 years, and an earlier onset and faster progress of bone loss and muscle degradation in women.

Systematic Review and Meta-Analysis on Optimal Timing of Surgery for Acute Symptomatic Metastatic Spinal Cord Compression

Introduction: Symptomatic acute metastatic spinal epidural cord compression (MSCC) is an emergency that requires multimodal attention. However, there is no clear consensus on the appropriate timing for surgery. Therefore, to address this issue, we conducted a systematic review and meta-analysis of the literature to evaluate the outcomes of different surgery timings. Methods: We searched multiple databases for studies involving adult patients suffering from symptomatic MSCC who underwent decompression with or without fixation. We analyzed the data by stratifying them based on timing as emergent (≤24 h vs. >24 h) and urgent (≤48 h vs. >48 h). The analysis also considered adverse postoperative medical and surgical events. The rates of improved outcomes and adverse events were pooled through a random-effects meta-analysis. Results: We analyzed seven studies involving 538 patients and discovered that 83.0% (95% CI 59.0-98.2%) of those who underwent urgent decompression showed an improvement of ≥1 point in strength scores. Adverse events were reported in 21% (95% CI 1.8-51.4%) of cases. Patients who underwent emergent surgery had a 41.3% (95% CI 20.4-63.3%) improvement rate but a complication rate of 25.5% (95% CI 15.9-36.3%). Patients who underwent surgery after 48 h showed 36.8% (95% CI 12.2-65.4%) and 28.6% (95% CI 19.5-38.8%) complication rates, respectively. Conclusion: Our study highlights that a 48 h window may be the safest and most beneficial for patients presenting with acute MSCC and a life expectancy of over three months.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT

PURPOSE

To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.

METHODS

We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC.

RESULTS

Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001).

CONCLUSION

A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

Design and 3D printing of novel titanium spine rods with lower flexural modulus and stiffness profile with optimised imaging compatibility

PURPOSE

To manufacture and test 3D printed novel design titanium spine rods with lower flexural modulus and stiffness compared to standard solid titanium rods for use in metastatic spine tumour surgery (MSTS) and osteoporosis.

METHODS

Novel design titanium spine rods were designed and 3D printed. Three-point bending test was performed to assess mechanical performance of rods, while a French bender was used to assess intraoperative rod contourability. Furthermore, 3D printed spine rods were tested for CT & MR imaging compatibility using phantom setup.

RESULTS

Different spine rod designs generated includes shell, voronoi, gyroid, diamond, weaire-phelan, kelvin, and star. Tests showed 3D printed rods had lower flexural modulus with reduction ranging from 2 to 25% versus standard rod. Shell rods exhibited highest reduction in flexural modulus of 25% (~ 77.4 GPa) and star rod exhibited lowest reduction in flexural modulus of 2% (100.8GPa). 3D printed rod showed reduction in stiffness ranging from 40 to 59%. Shell rod displayed highest reduction in stiffness of 59% (179.9 N/mm) and gyroid had least reduction in stiffness of 40% (~ 259.2 N/mm). Rod bending test showed that except gyroid, other rod designs demonstrated lesser bending difficulty versus standard rod. All 3D printed rods demonstrated improved CT/MR imaging compatibility with reduced artefacts versus standard rod.

CONCLUSION

By utilising novel design approach, we successfully generated a spine rod design portfolio with lower flexural modulus/stiffness profile and better CT/MR imaging compatibility for potential use in MSTS/other conditions such as osteoporosis. Thus, exploration of new rod designs in surgical application could enhance treatment outcome and improve quality of life for patients.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT

Introduction

Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.

Methods

Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated.

Results

Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001).

Conclusion

Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

Current and Emerging Approaches for Spine Tumor Treatment

Spine tumors represent a significant social and medical problem, affecting the quality of life of thousands of patients and imposing a burden on healthcare systems worldwide. Encompassing a wide range of diseases, spine tumors require prompt multidisciplinary treatment strategies, being mainly approached through chemotherapy, radiotherapy, and surgical interventions, either alone or in various combinations. However, these conventional tactics exhibit a series of drawbacks (e.g., multidrug resistance, tumor recurrence, systemic adverse effects, invasiveness, formation of large bone defects) which limit their application and efficacy. Therefore, recent research focused on finding better treatment alternatives by utilizing modern technologies to overcome the challenges associated with conventional treatments. In this context, the present paper aims to describe the types of spine tumors and the most common current treatment alternatives, further detailing the recent developments in anticancer nanoformulations, personalized implants, and enhanced surgical techniques.

Biological and Clinical Aspects of Metastatic Spinal Tumors

Simple Summary

Spine metastases are a common life-threatening complication of advanced-stage malignancies and often result in poor prognosis. Symptomatic spine metastases develop in the course of about 10% of malignant neoplasms. Therefore, it is essential for contemporary medicine to understand metastatic processes in order to find appropriate, targeted therapeutic options. Our literature review aimed to describe the up-to-date knowledge about the molecular pathways and biomarkers engaged in the spine’s metastatic processes. Moreover, we described current data regarding bone-targeted treatment, the emerging targeted therapies, radiotherapy, and immunotherapy used for the treatment of spine metastases. We hope that knowledge comprehensively presented in our review will contribute to the development of novel drugs targeting specific biomarkers and pathways. The more we learn about the molecular aspects of cancer metastasis, the easier it will be to look for treatment methods that will allow us to precisely kill tumor cells.

Abstract

Spine metastases are a common life-threatening complication of advanced-stage malignancies and often result in poor prognosis. Symptomatic spine metastases develop in the course of about 10% of malignant neoplasms. Therefore, it is essential for contemporary medicine to understand metastatic processes in order to find appropriate, targeted therapeutic options. Thanks to continuous research, there appears more and more detailed knowledge about cancer and metastasis, but these transformations are extremely complicated, e.g., due to the complexity of reactions, the variety of places where they occur, or the participation of both tumor cells and host cells in these transitions. The right target points in tumor metastasis mechanisms are still being researched; that will help us in the proper diagnosis as well as in finding the right treatment. In this literature review, we described the current knowledge about the molecular pathways and biomarkers engaged in metastatic processes involving the spine. We also presented a current bone-targeted treatment for spine metastases and the emerging therapies targeting the discussed molecular mechanisms.

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.