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Machine Learning-Based Detection of EGFR Mutation and HER2 Overexpression in Metastatic Brain Adenocarcinoma:
Mohammad Sadra Gholami Chahkand1, Mohammad Amin Karimi2, Komeil Aghazadeh-Habashi3
1Student Research Committee, Golestan University of Medical Sciences, Gorgan, Iran.
Background And Aim:
Brain metastases (BMs) are the most common intracranial malignancy, often arising from lung, breast, and melanoma cancers. Receptor tyrosine kinases, such as EGFR and HER2, drive tumor progression and resistance to therapy. Noninvasive detection of these biomarkers, especially in brain metastases, is crucial due to challenges with traditional biopsy methods. This systematic review and meta-analysis assess machine learning (ML)-based models for detecting EGFR mutations and HER2 overexpression in metastatic brain adenocarcinoma using MRI-derived radiomic features.
Methods:
A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. Studies were identified via PubMed, Scopus, and Web of Science, focusing on ML applications to MRI radiomics for detecting EGFR and HER2 in brain metastases. Data on study design, imaging modality, model type, sample size, and performance metrics were extracted. Subgroup analyses were performed by model type (deep learning vs. classical ML) and sample size (<150 vs. ≥150 participants). A random-effects model was used to pool performance metrics, and risk of bias was assessed using the RoB 2 tool. STATA version 18 and Python 3.10 were used for analyses and visualizations.
Results:
Of 383 identified studies, 31 (7925 participants) met the inclusion criteria. The pooled analysis showed strong diagnostic performance: AUC = 0.84, accuracy = 0.86, and sensitivity = 0.83. Subgroup analysis revealed higher AUC and accuracy in deep learning models compared with classical ML. Sensitivity analysis also indicated improved AUC in studies with larger sample sizes (≥150), though variability remained. No evidence of heterogeneity or publication bias was detected.
Conclusion:
ML models demonstrate strong diagnostic performance for detecting EGFR and HER2 in metastatic brain adenocarcinoma, supporting their potential as noninvasive diagnostic tools. However, these findings should be interpreted considering methodological heterogeneity and the limited use of external validation. Further prospective, multicenter studies are warranted to confirm their clinical applicability and generalizability.
Insights
Machine learning models show promise for detecting EGFR and HER2 biomarkers in brain metastases using MRI radiomics. These noninvasive tools offer strong diagnostic performance, aiding in cancer treatment decisions.
Area of Science:
- Neuro-oncology
- Radiology
- Artificial Intelligence
Background:
- Brain metastases (BMs) are common intracranial malignancies, often driven by receptor tyrosine kinases like EGFR and HER2.
- Noninvasive detection of these biomarkers in BMs is critical due to biopsy challenges.
- This study evaluates machine learning (ML) models using MRI radiomics for biomarker detection.
Purpose of the Study:
- To systematically review and meta-analyze ML-based models for detecting EGFR mutations and HER2 overexpression in metastatic brain adenocarcinoma.
- To assess the diagnostic performance of these ML models using MRI-derived radiomic features.
Main Methods:
- Systematic review and meta-analysis following PRISMA 2020 guidelines.
- Searched PubMed, Scopus, and Web of Science for studies on ML and MRI radiomics in brain metastases.
- Extracted data on study design, imaging, models, sample size, and performance; conducted subgroup analyses by model type and sample size.
Main Results:
- Included 31 studies with 7925 participants; pooled analysis showed strong performance (AUC=0.84, accuracy=0.86, sensitivity=0.83).
- Deep learning models outperformed classical ML models in AUC and accuracy.
- Larger sample sizes (≥150) correlated with improved AUC, with no detected heterogeneity or publication bias.
Conclusions:
- ML models demonstrate significant potential as noninvasive tools for detecting EGFR and HER2 in metastatic brain adenocarcinoma.
- Methodological heterogeneity and limited external validation necessitate cautious interpretation.
- Further prospective, multicenter studies are required to confirm clinical applicability and generalizability.
