Cerebral Metastases of Lung Cancer Mimicking Multiple Ischaemic Lesions – A Case Report and Review of Literature

Zacharzewska-Gondek, A.; Maksymowicz, Hanna; Szymczyk, Małgorzata; Sąsiadek, Marek; Bladowska, Joanna

doi:10.12659/pjr.902213

Cited by 3 publications

(1 citation statement)

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“…Topographic delineation of brain regions that are likely to infarct together may also help with differentiation of ischemic infarct from other pathologies presenting with restricted diffusion (e.g. hypercellular metastasis) based on topographic distribution of suspicious lesions (Zacharzewska-Gondek et al, 2017). Thus, those lesions with reduced diffusion, which are not conforming to boundaries of stroke atlas, are more likely representing non-infarct lesions such as hypercellular metastases.…”

Section: Discussionmentioning

confidence: 99%

Stroke atlas of the brain: Voxel-wise density-based clustering of infarct lesions topographic distribution

Wang

Juliano

Liew

et al. 2019

NeuroImage: Clinical

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Objective The supply territories of main cerebral arteries are predominantly identified based on distribution of infarct lesions in patients with large arterial occlusion; whereas, there is no consensus atlas regarding the supply territories of smaller end-arteries. In this study, we applied a data-driven approach to construct a stroke atlas of the brain using hierarchical density clustering in large number of infarct lesions, assuming that voxels/regions supplied by a common end-artery tend to infarct together. Methods A total of 793 infarct lesions on MRI scans of 458 patients were segmented and coregistered to MNI-152 standard brain space. Applying a voxel-wise data-driven hierarchical density clustering algorithm, we identified those voxels that were most likely to be part of same infarct lesions in our dataset. A step-wise clustering scheme was applied, where the clustering threshold was gradually decreased to form the first 20 mother (>50 cm 3 ) or main (1–50 cm 3 ) clusters in addition to any possible number of tiny clusters (<1 cm 3 ); and then, any resultant mother clusters were iteratively subdivided using the same scheme. Also, in a randomly selected 2/3 subset of our cohort, a bootstrapping cluster analysis with 100 permutations was performed to assess the statistical robustness of proposed clusters. Results Approximately 91% of the MNI-152 brain mask was covered by 793 infarct lesions across patients. The covered area of brain was parcellated into 4 mother, 16 main, and 123 tiny clusters at the first hierarchy level. Upon iterative clustering subdivision of mother clusters, the brain tissue was eventually parcellated into 1 mother cluster (62.6 cm 3 ), 181 main clusters (total volume 1107.3 cm 3 ), and 917 tiny clusters (total volume of 264.8 cm 3 ). In bootstrap analysis, only 0.12% of voxels, were labelled as “unstable” – with a greater reachability distance in cluster scheme compared to their corresponding mean bootstrapped reachability distance. On visual assessment, the mother/main clusters were formed along supply territories of main cerebral arteries at initial hierarchical levels, and then tiny clusters emerged in deep white matter and gray matter nuclei prone to small vessel ischemic infarcts. Conclusions Applying voxel-wise data-driven hierarchical density clustering on a large number of infarct lesions, we have parcellated the brain tissue into clusters of voxels that tend to be part of same infarct lesion, and presumably representing end-arterial supply territories. This hierarchical stroke atlas of the brain is shared publicly, and can potentially be applied for future infarct location-outcome analysis.

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Section: Discussionmentioning

confidence: 99%