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Updated: Feb 7, 2026
Studying the Stoichiometry of Epidermal Growth Factor Receptor in Intact Cells using Correlative Microscopy
Published on: September 11, 2015
Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by
Jun-Feng Xiong1, Tian-Ying Jia2, Xiao-Yang Li2
11 Department of Biomedical Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University , Shanghai , China.
Objective::
Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sensitive mutations of the epidermal growth factor receptor (EGFR) gene. We constructed three-dimensional convolutional neural networks (CNN) to analyze underlying patterns in CT images that could indicate that EGFR gene mutation status but are invisible to human eyes.
Methods::
From 2012 to 2015, 503 Chinese patients with lung adenocarcinoma that had underwent surgery were included. Pathological types and EGFR mutation status were tested from surgical resections. EGFR mutations (exon 19 deletion or exon 21 L858R) were found in 215/345 (62.3%) and 91/158 (57.6%) patients in the training and independent validation set, respectively. CT images were taken before any invasive operation. The patients were randomly chosen to train the CNNs or validate the CNNs' performance. The performance was quantified using area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
Results::
The CNNs showed an AUC of 0.776 (range: 0.702-0.849, p< 0.0001) in the independent validation set and a fusion model of CNNs and clinical features (sex and smoking history) showed an AUC of 0.838 (range: 0.778-0.899, p< 0.0001), accuracy of 77.2%, sensitivity of 75.8% and specificity of 79.1% at the best diagnostic decision point.
Conclusion::
The CNN exhibits potential ability to identify EGFR mutation status in patients with lung adenocarcinoma which might help make clinical decisions.
Advances In Knowledge::
The CNN showed some diagnostic power and its performance could be further improved by increasing the training set, optimizing the network structure and training strategy. Medical image based CNN has the potential to reflect spatial heterogeneity.
Insights
This study used deep learning (CNN) to analyze CT scans for predicting epidermal growth factor receptor (EGFR) gene mutations in lung adenocarcinoma patients, aiding treatment decisions.
Area of Science:
- Oncology
- Radiology
- Artificial Intelligence
Background:
- Genetic mutations, particularly in the epidermal growth factor receptor (EGFR) gene, are crucial for guiding treatment decisions in lung adenocarcinoma.
- Identifying EGFR mutation status non-invasively can significantly impact patient management and treatment selection.
Purpose of the Study:
- To develop and evaluate three-dimensional convolutional neural networks (CNNs) for predicting EGFR gene mutation status in lung adenocarcinoma patients using CT images.
- To assess the potential of CNNs to identify imaging patterns indicative of EGFR mutations that are not discernible to the human eye.
Main Methods:
- A cohort of 503 Chinese lung adenocarcinoma patients who underwent surgery between 2012 and 2015 was retrospectively analyzed.
- Three-dimensional CNNs were trained and validated on CT images acquired before surgery to predict EGFR mutations (exon 19 deletion or exon 21 L858R).
- Performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
Main Results:
- The CNN model achieved an AUC of 0.776 in the independent validation set.
- A fusion model combining CNNs with clinical features (sex, smoking history) improved performance, yielding an AUC of 0.838, with 77.2% accuracy, 75.8% sensitivity, and 79.1% specificity.
- These results indicate the CNN's ability to identify potential indicators of EGFR mutation status from CT images.
Conclusions:
- CNNs demonstrate potential in predicting EGFR mutation status in lung adenocarcinoma, offering a non-invasive approach to aid clinical decision-making.
- Further improvements in CNN performance can be achieved through larger training datasets and optimized network architectures.
- AI-based analysis of medical images holds promise for revealing spatial heterogeneity relevant to cancer characteristics.
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