Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study

Li, Yuting; Yu, Ruize; Chang, Huan; Yan, Wanying; Wang, Dawei; Li, Fuyan; Cui, Yi; Wang, Yong; Wang, Xiao; Yan, Qingqing; Liu, Xinhui; Jia, Wenjing; Zeng, Qingshi

doi:10.1007/s10278-024-00988-0

Cited by 2 publications

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Identification of the Pathological Types of Brain Metastasis from Lung Cancer Based on Multiparametric MRI Radiomics: A Feasibility Study

Sui,

Yin,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Objectives To differentiate brain metastases (BMs) from non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) and BMs due to the adenocarcinoma (AD) and non-adenocarcinoma (NAD) subtypes using radiomic features derived from multiparametric magnetic resonance imaging (MRI). Methods 276 patients with BMs, including 98 with SCLC and 178 with NSCLC, were randomly divided into training (193 cases) and validation (83 cases) sets in a ratio of 7:3. Of the 178 patients with NSCLC, 155 were from primary AD and 23 from NAD. These were also randomly divided into training (124 cases) and validation (54 cases) sets. A logistic regression analysis was used to construct classification models based on radiomics features that were extracted from T1 weighted contrast-enhanced (T1CE), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging (DWI) images. The receiver operating characteristic curve (ROC) was used to evaluate the diagnostic efficiency. Results Multiparametric combined-sequence MRI radiomics features based on TICE, FLAIR, and DWI images were highly specific in distinguishing brain metastases originating from different types of lung cancers. In the training and validation sets, the area under the curves (AUCs) of the model for the classification of SCLC and NSCLC brain metastasis were 0.765 (95% CI 0.711, 0.822) and 0.762 (95% CI 0.671, 0.845), respectively; the AUC values of the prediction models combining the three sequences in differentiating AD from NAD BMs were 0.861 (95% CI 0.756, 0.951) and 0.851 (95% CI 0.649, 0.984), respectively. Conclusion The radiomics classification method based on the combination of multiple MRI sequences may be used for differentiating between the various lung cancer BMs.

show abstract

Identification of the Pathological Types of Brain Metastasis from Lung Cancer Based on Multiparametric MRI Radiomics: A Feasibility Study

Sui,

Yin,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Effect of adding individualized health education for patients with brain metastasis of lung cancer undergoing radiotherapy, as measured by MRI and cognitive testing

2024

Am J Transl Res

View full text Add to dashboard Cite

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study

Cited by 2 publications

References 39 publications

Identification of the Pathological Types of Brain Metastasis from Lung Cancer Based on Multiparametric MRI Radiomics: A Feasibility Study

Identification of the Pathological Types of Brain Metastasis from Lung Cancer Based on Multiparametric MRI Radiomics: A Feasibility Study

Effect of adding individualized health education for patients with brain metastasis of lung cancer undergoing radiotherapy, as measured by MRI and cognitive testing

Contact Info

Product

Resources

About