STUDY THE ROLE OF MAGNETIC CONTRAST IN THE DIAGNOSIS OF BRAIN METASTASES IN LUNG CANCER PATIENTS TREATED AT THE NATIONAL LUNG HOSPITAL

Văn Công Cung1,
1 Lung central hospital

Main Article Content

Abstract

Objectives: Describe sequential magnetic resonance imaging (MRI) on sequences of early post-injection T1W, post-injection T2FLAIR, late post-injection T1W and compare the ability to detect brain metastases of each pair of the above pulse sequence in primary lung cancer (LC) patients treated at the National Lung Hospital. Subjects: 100 patients (patients) were diagnosed with primary LC by pathology, indicated for MRI to screen for brain metastases. Methods:prospective, descriptive, cross-sectional. Results: Average age: 62.2 ± 8.9; male/female is 2.1:1; The size of the tumor from 0.8 to 23 mm. Lesions appear in: occipital lobe (49.2%); parietal lobe (34.9%); frontal lobe (23.8%); temporal lobe (11.1%); cerebellum (7.9%); brainstem (4.7%). Absorb gado contrast media evenly: 34.9%; irregular: 7.9%; ring form: 46.1%; combination of at least 2 forms: 11.1%; 58.7% had cerebral edema around the lesion; ventricular dilatation 11.1%. The comparison of "clearer" between early T1W pulse sequence with T2Flair and late T1W is statistically significant. The T1W pulse sequence early detected 56 patients (56%) with brain metastases; pulse sequence T2Flair 61 BN (61%); Late T1 pulse sequence 63 cases (63%) but the comparison of additional detection ability in each pair of pulse sequences is not statistically significant (small sample size). The ability to detect the number of lesions at >3 lesions per individual when comparing early T1W with T2Flair; T2Flair with late T1W and late T1W with early T1W were statistically significant. Conclusion: Use sequential pulse sequences after injection: early T1W, T2Flair; Late T1W is important in detecting brain metastases.

Article Details

References

1. Zhou Z, Sanders JW, Johnson JM, et al. Computer aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single - Shot Detectors. Radiology. 2020 May; 295(2):407-415. doi: 10.1148/radiol.2020191479. Epub 2020 Mar 17. PMID: 32181729
2. Hsu DG, Ballangrud Å, Shamseddine A, et al. Automatic segmentation of brain metastases using T1 magnetic resonance and computed tomography images. Phys Med Biol. 2021 Aug 26;66(17). doi: 10.1088/1361 6560/ac1835.PMID: 34315148
3. Dikici E, Ryu JL, Demirer M, et al. Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI. IEEE J Biomed Health Inform. 2020 Oct;24(10):2883-2893. doi: 10.1109/JBHI.2020.2982103. Epub 2020 Mar 23. PMID: 32203040
4. Cho SJ, Sunwoo L, Baik SH, et al. Brain metastasis detection using machine learning: a systematic review and meta-analysis. Neuro Oncol. 2021 Feb 25;23(2):214-225. doi: 10.1093/neuonc/noaa232.PMID: 33075135
5. Bahadure NB, Ray AK, Thethi HP. Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm. J Digit Imaging. 2018 Aug;31(4):477-489. doi: 10.1007/s10278-018-0050-6. PMID: 29344753
6. Liheng M, Guofan X, Balzano RF, et al. The value of DTI: achieving high diagnostic performance for brain metastasis. Radiol Med. 2021 Feb;126(2):291-298. doi: 10.1007/s11547-020-01243-6. Epub 2020 Jun 20. PMID: 32564269
7. Ono Y, Abe K, Hayashi M, Chernov MF, Okada Y, Sakai S, Takakura K. Optimal visualization of multiple brain metastases for gamma knife radiosurgery. Acta Neurochir Suppl. 2013;116:159-66. doi: 10.1007/978-3-7091-1376-9_25.PMID:23417475