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

ABSTRACT: Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

SUBMITTER: Kuah T

PROVIDER: S-EPMC9265325 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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

Kuah Tricia T Vellayappan Balamurugan A BA Makmur Andrew A Nair Shalini S Song Junda J Tan Jiong Hao JH Kumar Naresh N Quek Swee Tian ST Hallinan James Thomas Patrick Decourcy JTPD

Cancers 20220705 13

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their sui ...[more]

PMID: 35805059

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Project description:BackgroundMetastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.PurposeTo develop a DL model for automated classification of MESCC on MRI.Materials and methodsPatients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet's kappa) and sensitivity/specificity were calculated.ResultsOverall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92-0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94-0.95, p < 0.001) compared to the reference standard.ConclusionA DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Project description:BackgroundMetastatic spinal cord compression (MSCC) treatment depends on life expectancies. Data regarding palliative decompression outcomes is scarce. We demonstrate that surgical timing has a significant impact on survival in MSCC patients treated with palliative decompression.MethodsEighty-nine consecutive MSCC patients at a tertiary referral medical center were enrolled between January 2012 and February 2016. Wide laminectomy was performed for tumors invading the vertebral body. Debulking surgery was done for tumors damaging the posterior column of the spine. Patient records were retrospectively analyzed.ResultsBetter survival was observed in patients with preoperative intact motor function (Group A, n = 37) than in those with motor deficit (Group B, n = 52, p = 0.0031). In Group B, survival was better in those who underwent surgery within 7 days of motor deficit onset than in those who underwent surgery 7 days after onset (p = 0.0444) and in postoperative ambulant patients than in nonambulant patients (p = 0.0120). In Group B, Frankel grade improved in patients who underwent surgery within 48 h than in those who underwent surgery after 48 h (p = 0.0992). Group A patients had a shorter hospital stay and higher revised Tokuhashi score than Group B patients. Overall survival was better in patients with a lower Tomita score (≤5, p = 0.0012), higher revised Tokuhashi score (≥9, p = 0.0009), better preoperative Frankel grade (p < 0.0001), and younger age (≤55 years, p = 0.0179). There were no significant differences in age, sex, tumor type, involved vertebrae level, Tomita score, intraoperative blood loss, operation time, incidence of infection, and postoperative complications between groups.ConclusionWe can improve the survival of MSCC patients with palliative decompression before motor deficits occur. After motor deficit onset, survival can still be improved with surgery within 7 days. Overall survival was better in patients aged ≤55 years.

Project description:BackgroundDegenerative cervical spinal cord compression is becoming increasingly prevalent, yet the MRI criteria that define compression are vague, and vary between studies. This contribution addresses the detection of compression by means of the Spinal Cord Toolbox (SCT) and assesses the variability of the morphometric parameters extracted with it.MethodsProspective cross-sectional study. Two types of MRI examination, 3 and 1.5 T, were performed on 66 healthy controls and 118 participants with cervical spinal cord compression. Morphometric parameters from 3T MRI obtained by Spinal Cord Toolbox (cross-sectional area, solidity, compressive ratio, torsion) were combined in multivariate logistic regression models with the outcome (binary dependent variable) being the presence of compression determined by two radiologists. Inter-trial (between 3 and 1.5 T) and inter-rater (three expert raters and SCT) variability of morphometric parameters were assessed in a subset of 35 controls and 30 participants with compression.ResultsThe logistic model combining compressive ratio, cross-sectional area, solidity, torsion and one binary indicator, whether or not the compression was set at level C6/7, demonstrated outstanding compression detection (area under curve =0.947). The single best cut-off for predicted probability calculated using a multiple regression equation was 0.451, with a sensitivity of 87.3% and a specificity of 90.2%. The inter-trial variability was better in Spinal Cord Toolbox (intraclass correlation coefficient was 0.858 for compressive ratio and 0.735 for cross-sectional area) compared to expert raters (mean coefficient for three expert raters was 0.722 for compressive ratio and 0.486 for cross-sectional area). The analysis of inter-rater variability demonstrated general agreement between SCT and three expert raters, as the correlations between SCT and raters were generally similar to those of the raters between one another.ConclusionsThis study demonstrates successful semi-automated compression detection based on four parameters. The inter-trial variability of parameters established through two MRI examinations was conclusively better for Spinal Cord Toolbox compared with that of three experts' manual ratings.

Dataset Information

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

Publications

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

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