Abstract
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.
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Data Availability
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to patient confidentiality.
Abbreviations
- DL :
-
Deep learning
- BM :
-
Brain metastasis
- SCLC :
-
Small-cell lung cancer
- NSCLC :
-
Non-small-cell lung cancer
- ARFN :
-
Attention-guided residual fusion network
- AD :
-
Adenocarcinoma
- SCC :
-
Squamous cell carcinoma
- mp-MRI :
-
Multiparametric MRI
- T1WI :
-
T1-weighted images
- T2-FLAIR :
-
T2 fluid-attenuated inversion recovery
- T2WI :
-
T2-weighted images
- DWI :
-
Diffusion-weighted image
- CE-T1WI :
-
Contrast-enhanced T1WI
- ANTS :
-
Advanced Normalization Tools
- ROI :
-
Region of interest
- FTA :
-
Fourier transform augmentation
- SE :
-
Squeeze and Excitation
- ROC :
-
Receiver operating characteristic
- PPV :
-
Positive predictive value
- NPV :
-
Negative predictive value
- Grad-CAM :
-
Gradient-weighted Class Activation Mapping
- CI :
-
Confidence intervals
- CNN :
-
Convolutional neural network
- AUC :
-
Area under the receiver operating characteristic curve
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All authors contributed to the study conception and design. Yuting Li and Qingshi Zeng contributed to the study conception and design. Material preparation and data collection were performed by Wenjing Jia, Qingqing Yan, Huan Chang, Fuyan Li, Yi Cui, Xiao Wang, and Yong Wang, and data analysis was performed by Ruize Yu, Wanying Yan, Dawei Wang, and Yuting Li. The first draft of the manuscript was written by Yuting Li and Qingshi Zeng, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Li, Y., Yu, R., Chang, H. et al. Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study. J Digit Imaging. Inform. med. 37, 976–987 (2024). https://doi.org/10.1007/s10278-024-00988-0
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DOI: https://doi.org/10.1007/s10278-024-00988-0
