Dataset Information

Agreement and differentiation of intradural spinal cord lesions in dogs using magnetic resonance imaging.

ABSTRACT:

Background

Magnetic resonance imaging is the method of choice for diagnosing spinal cord neoplasia, but the accuracy of designating the relationship of a neoplasm to the meninges and agreement among observers is unknown.

Objectives

To determine agreement among observers and accuracy of diagnosis compared with histology when diagnosing lesion location based on relationship to the meninges.

Animals

Magnetic resonance images from 53 dogs with intradural extramedullary and intramedullary spinal neoplasms and 17 dogs with degenerative myelopathy.

Methods

Six observers were supplied with 2 sets of 35 images at different time points and asked to designate lesion location. Agreement in each set was analyzed using kappa (κ) statistics. We tabulated total correct allocations and calculated sensitivity, specificity, and likelihood ratios for location designation from images compared with known histologic location for lesions confined to 1 location only.

Results

Agreement in the first set of images was moderate (κ = 0.51; 95% confidence interval [CI], 0.43-0.58) and in the second, substantial (κ = 0.69; 95% CI, 0.66-0.79). In the accuracy study, 180 (75%) of the 240 diagnostic calls were correct. Sensitivity and specificity were moderate to high for all compartments, except poor sensitivity was found for intradural extramedullary lesions. Positive likelihood ratios were high for intradural extramedullary lesions and degenerative myelopathy.

Conclusions and clinical importance

Overall accuracy in diagnosis was reasonable, and positive diagnostic calls for intradural extramedullary lesions and negative calls for intramedullary lesions are likely to be helpful. Observers exhibited considerable disagreement in designation of lesions relationship to the meninges.

SUBMITTER: Krasnow MS

PROVIDER: S-EPMC8783334 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Deep Learning based Convolutional Neural Networks (CNNs) are the state-of-the-art machine learning technique with medical image data. They have the ability to process large amounts of data and learn image features directly from the raw data. Based on their training, these networks are ultimately able to classify unknown data and make predictions. Magnetic resonance imaging (MRI) is the imaging modality of choice for many spinal cord disorders. Proper interpretation requires time and expertise from radiologists, so there is great interest in using artificial intelligence to more quickly interpret and diagnose medical imaging data. In this study, a CNN was trained and tested using thoracolumbar MR images from 500 dogs. T1- and T2-weighted MR images in sagittal and transverse planes were used. The network was trained with unremarkable images as well as with images showing the following spinal cord pathologies: intervertebral disc extrusion (IVDE), intervertebral disc protrusion (IVDP), fibrocartilaginous embolism (FCE)/acute non-compressive nucleus pulposus extrusion (ANNPE), syringomyelia and neoplasia. 2,693 MR images from 375 dogs were used for network training. The network was tested using 7,695 MR images from 125 dogs. The network performed best in detecting IVDPs on sagittal T1-weighted images, with a sensitivity of 100% and specificity of 95.1%. The network also performed very well in detecting IVDEs, especially on sagittal T2-weighted images, with a sensitivity of 90.8% and specificity of 98.98%. The network detected FCEs and ANNPEs with a sensitivity of 62.22% and a specificity of 97.90% on sagittal T2-weighted images and with a sensitivity of 91% and a specificity of 90% on transverse T2-weighted images. In detecting neoplasms and syringomyelia, the CNN did not perform well because of insufficient training data or because the network had problems differentiating different hyperintensities on T2-weighted images and thus made incorrect predictions. This study has shown that it is possible to train a CNN in terms of recognizing and differentiating various spinal cord pathologies on canine MR images. CNNs therefore have great potential to act as a "second eye" for imagers in the future, providing a faster focus on the altered image area and thus increasing workflow in radiology.