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DOI: 10.4103/sajc.sajc_98_18
Patterns of brain metastasis in anaplastic lymphoma kinase - rearranged and epidermal growth factor receptor-mutated lung cancer patients in magnetic resonance imaging
Financial support and sponsorship This study was supported by Rajiv Gandhi Cancer Institute and Research Centre, New Delhi.![](https://www.thieme-connect.de/media/10.1055-s-00049561/201903/lookinside/thumbnails/10-4103_sajc-sajc_98_18_189-1.jpg)
Abstract
Introduction: The optimal management of neuroparenchymal lesions in cases of lung cancer is exigent as this frequent yet notorious complication negatively impacts the morbidity and mortality index. Aims: This study is aimed at recognizing various patterns of neuroparenchymal metastasis in patients of lung cancer with epidermal growth factor receptor (EGFR)- and anaplastic lymphoma kinase (ALK)-positive mutations. Material and Methods: The radiological findings of the neuroparenchymal lesions were analyzed and the statistical data were charted. We identified two groups of patients with neuroparenchymal lesions among a cohort of 340 patients having EGFR-positive (68) and ALK-positive (24) mutations (total: 24 + 68 = 92). Results: We observed that among the ALK group, leptomeningeal spread was less compared to EGFR group (2/24 as opposed to 18/68). Morphological heterogeneity and central necrosis in the parenchymal lesion which were associated with unfavorable outcomes were predominant in ALK group (8/24) as opposed to EGFR group (2/68). Ancillary findings but pertinent to survival and morbidity such as presence of perilesional edema, hemorrhage, and hydrocephalus on magnetic resonance imaging were also analyzed. The mutation-specific differential imaging spectrum could be attributed to biological differences between these cancers.
Key words
Anaplastic lymphoma kinase mutation - epidermal growth factor receptor - lung cancer - magnetic resonance patterns - neuroparenchymalPublication History
Article published online:
21 December 2020
© 2019. MedIntel Services Pvt Ltd. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/.)
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