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Metastatic spinal cord compression as the first sign of malignancy.

ABSTRACT: Background and purpose - Metastatic spinal cord compression (MSCC) as the initial manifestation of malignancy (IMM) limits the time for diagnostic workup; most often, treatment is required before the final primary tumor diagnosis. We evaluated neurological outcome, complications, survival, and the manner of diagnosing the primary tumor in patients who were operated for MSCC as the IMM. Patients and methods - Records of 69 consecutive patients (51 men) who underwent surgery for MSCC as the IMM were reviewed. The patients had no history of cancer when they presented with pain (n = 2) and/or neurological symptoms (n = 67). Results - The primary tumor was identified in 59 patients. In 10 patients, no specific diagnosis could be established, and they were therefore defined as having cancer of unknown primary tumor (CUP). At the end of the study, 16 patients were still alive (median follow-up 2.5 years). The overall survival time was 20 months. Patients with CUP had the shortest survival (3.5 months) whereas patients with prostate cancer (6 years) and myeloma (5 years) had the longest survival. 20 of the 39 patients who were non-ambulatory preoperatively regained walking ability, and 29 of the 30 ambulatory patients preoperatively retained their walking ability 1 month postoperatively. 15 of the 69 patients suffered from a total of 20 complications within 1 month postoperatively. Interpretation - Postoperative survival with MSCC as the IMM depends on the type of primary tumor. Surgery in these patients maintains and improves ambulatory function.

SUBMITTER: Wanman J

PROVIDER: S-EPMC5499341 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Metastatic spinal cord compression as the first sign of malignancy.

Wänman Johan J Grabowski Pawel P Nyström Helena H Gustafsson Patrik P Bergh Anders A Widmark Anders A Crnalic Sead S

Acta orthopaedica 20170511 4

Background and purpose - Metastatic spinal cord compression (MSCC) as the initial manifestation of malignancy (IMM) limits the time for diagnostic workup; most often, treatment is required before the final primary tumor diagnosis. We evaluated neurological outcome, complications, survival, and the manner of diagnosing the primary tumor in patients who were operated for MSCC as the IMM. Patients and methods - Records of 69 consecutive patients (51 men) who underwent surgery for MSCC as the IMM we ...[more]

PMID: 28492105

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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:Simple Summary Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy, and early diagnosis is important to prevent irreversible neurological injury. MRI is the mainstay of diagnosis for MESCC, but it is expensive, and routine screening of asymptomatic patients is not feasible. Staging CT studies are performed routinely as part of the cancer diagnosis and represent an opportunity for earlier diagnosis and treatment planning. In this study, we trained deep learning models for automatic MESCC classification on staging CT studies using spine MRI and manual radiologist labels as the reference standard. On a test set, the DL models showed almost-perfect interobserver agreement for the classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two radiologists, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Abstract Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2–7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.

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.

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Metastatic spinal cord compression as the first sign of malignancy.

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Metastatic spinal cord compression as the first sign of malignancy.

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