PT  - JOURNAL ARTICLE
AU  - O. Shearkhani
AU  - A. Khademi
AU  - A. Eilaghi
AU  - S.-P. Hojjat
AU  - S.P. Symons
AU  - C. Heyn
AU  - M. Machnowska
AU  - A. Chan
AU  - A. Sahgal
AU  - P.J. Maralani
TI  - Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field
AID  - 10.3174/ajnr.A5352
DP  - 2017 Sep 07
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2017/09/07/ajnr.A5352.short
4100  - http://www.ajnr.org/content/early/2017/09/07/ajnr.A5352.full
AB  - BACKGROUND AND PURPOSE: Accurate follow-up of metastatic brain tumors has important implications for patient prognosis and management. The aim of this study was to develop and evaluate the accuracy of a semiautomated algorithm in detecting growing or shrinking metastatic brain tumors on longitudinal brain MRIs.MATERIALS AND METHODS: We used 50 pairs of successive MR imaging datasets, 30 on 1.5T and 20 on 3T, containing contrast-enhanced 3D T1-weighted sequences. These yielded 150 growing or shrinking metastatic brain tumors. To detect them, we completed 2 major steps: 1) spatial normalization and calculation of the Jacobian operator field to quantify changes between scans, and 2) metastatic brain tumor candidate segmentation and detection of volume-changing metastatic brain tumors with the Jacobian operator field. Receiver operating characteristic analysis was used to assess the detection accuracy of the algorithm, and it was verified with jackknife resampling. The reference standard was based on detections by a neuroradiologist.RESULTS: The areas under the receiver operating characteristic curves were 0.925 for 1.5T and 0.965 for 3T. Furthermore, at its optimal performance, the algorithm achieved a sensitivity of 85.1% and 92.1% and specificity of 86.7% and 91.3% for 1.5T and 3T, respectively. Vessels were responsible for most false-positives. Newly developed or resolved metastatic brain tumors were a major source of false-negatives.CONCLUSIONS: The proposed algorithm could detect volume-changing metastatic brain tumors on longitudinal brain MRIs with statistically high accuracy, demonstrating its potential as a computer-aided change-detection tool for complementing the performance of radiologists, decreasing inter- and intraobserver variability, and improving efficacy.AbbreviationsAUCarea under the curve3D-T1-Gadcontrast-enhanced 3D T1-weightedΔMBTvolume-changing MBTΔMBTosnewly developed or resolved MBTΔMBTtschanging MBT present on both baseline and follow-up scansFPRfalse-positive rateJOFJacobian operator fieldMBTmetastatic brain tumorROCreceiver operating characteristicVCRvolume change ratio