The normal appearance of CT myelograms

Rocos, Brett; Evans, Donald; Rajayogeswaran, Brathaban; Hutchinson, Morgan

doi:10.1007/s00586-019-06287-5

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In some cases (e.g., when MRI findings were inconclusive or ambiguous), CTM was an adjunct imaging modality in clinical practice [ 47 ], as it showed advantages in detecting multi-level LSS [ 48 ]. Moreover, with the increasing use of spinal instrumentation, CTM still remains an alternative imaging method in the investigation of LSS [ 49 ]. However, young physicians are unfamiliar with interpreting this old-fashioned imaging modality, and even experienced physicians may fail to objectively and quantitatively assess LSS.…”

Section: Discussionmentioning

confidence: 99%

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

Fan,

Wang,

et al. 2023

Diagnostics

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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.

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Section: Discussionmentioning

confidence: 99%

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

Fan,

Wang,

et al. 2023

Diagnostics

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Resurgence of the Role of Computed-Tomography Myelogram in the Era of MRI for Surgical Decision-Making of Lumbar Degenerative Disc Disorder

et al. 2021

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Background: Since the introduction of magnetic resonance imaging (MRI) into clinical practice in the mid-1980s, the role of computed tomography myelography (CTM) has become less important in spinal diagnostics but remains a method that is probably even superior to MRI for special clinical issues. The study aims to report the diagnostic utility of CTM as an adjunct to MRI in lumbar degenerative disc disorder (DDD).Methods: Included were 20 patients who presented with symptomatic DDD but with MRI findings that did not correlate with the clinical features. These patients underwent CTM as an additional imaging technique to aid preoperative surgical decision-making. Both imaging modalities were compared for the identification of the impinging pathology as well as the number of levels of compression.Results: MRI revealed compression and/or impingement at 38 levels, whereas CTM revealed these at 29 levels. Of 20 patients, 18 underwent surgery, and a total of 29 levels were decompressed as localized in the CTM. The visual analog scale (VAS) score for back pain and leg pain at baseline were 6 6 0.7 and 76 0.4, respectively, and at 6 months postintervention (surgical/conservative) were 2 6 0.8 and 0.3 6 0.1, respectively. The Oswestry Disability Index scores at baseline and 6 months postintervention were 56 6 6.9 and 18 6 4.2, respectively (P , .0001). There was agreement on the number of levels between MRI and CTM in 10 patients (50%). MRI overestimated the number of involved levels in 9 patients (45%), whereas in the remaining 1 patient (5%), MRI underestimated the number of involved levels. The weighted j value for agreement between MRI and CTM on the number of levels involved necessitating decompression was 0.4 (95% CI, 0.18-0.77; P ¼ .0009).Conclusions: CTM has a role as an adjunct imaging modality to formulate an effective management plan in patients presenting with symptomatic lumbar DDD in cases where MRI findings are inconclusive and ambiguous.

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CT myelography

Knipe¹

2023

Radiopaedia.org

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The normal appearance of CT myelograms

Cited by 3 publications

References 13 publications

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

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

Resurgence of the Role of Computed-Tomography Myelogram in the Era of MRI for Surgical Decision-Making of Lumbar Degenerative Disc Disorder

CT myelography

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