Application and research progress of MRI radiomics in brain metastases from lung cancer

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SUI Lianyu MENG Huan WANG Jianing YIN Xiaoping

Cite this article as: SUI L Y, MENG H, WANG J N, et al. Application and research progress of MRI radiomics in brain metastases from lung cancer[J]. Chin J Magn Reson Imaging, 2025, 16(2): 185-192. DOI:10.12015/issn.1674-8034.2025.02.030.

Abstract

[Abstract] Brain metastases (BMs) are the most prevalent intracranial malignancies, with lung cancer BMs being particularly common in clinical practice, closely associated with the poor prognosis and high mortality of lung cancer. Therefore, precise diagnosis and treatment of lung cancer BMs are of paramount importance for their clinical management. Magnetic resonance imaging (MRI) has been widely regarded as the imaging gold standard for the diagnosis and prognosis assessment of BMs due to its high sensitivity and specificity. MRI radiomics enables a more detailed characterization of the internal structure and heterogeneity of tumors through high-throughput feature extraction methods. The quantitative imaging features extracted from MRI are closely related to the biological behavior and clinical prognosis of tumors, providing clinicians with richer decision-support information to enhance the accuracy of diagnosis and personalization of treatment. Recent studies have shown that MRI radiomics demonstrates great potential in improving the accuracy and efficiency of clinicians in diagnosing, classifying, treating, and predicting the prognosis of lung cancer BMs. This article aims to comprehensively summarize the latest applications of MRI radiomics in lung cancer BMs in terms of data segmentation processing and model establishment, in order to provide insights for research in this emerging field.

[Keywords] brain metastases；lung cancer；magnetic resonance imaging；radiomics；deep learning；artificial intelligence

Contributor Information

SUI Lianyu¹ MENG Huan¹ WANG Jianing¹ YIN Xiaoping^{1,
2*}

¹ Clinical Medicine School of Hebei University/Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China

² Department of Radiology, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, the Affiliated Hospital of Hebei University, Baoding 071000, China

Corresponding author: YIN X P, E-mail: yinxiaoping78@sina.com

Conflicts of interest None.

Publication Information

Received 2024-10-22

Accepted 2025-02-10

DOI: 10.12015/issn.1674-8034.2025.02.030

Text

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