An automatic end-to-end pipeline for CT image-based EGFR mutation status classification

Lin Tian; Rong Yuan

doi:10.1117/12.2512465

15 March 2019 An automatic end-to-end pipeline for CT image-based EGFR mutation status classification

Lin Tian, Rong Yuan

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109492Q (2019) https://doi.org/10.1117/12.2512465
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

The epidermal growth factor receptor (EGFR) mutation status play a key role in clinical decision support and prognosis for non-small-cell lung cancer (NSCLC). In this study, we present an automatic end-to-end pipeline to classify the EGFR mutation status according to the features extracted from medical images via deep learning. We tried to solve this problem with three steps: (I) locating tumor candidates via a 3D convolutional neural network (CNN), (II) extracting features via pre-trained lower convolutional layers (layers before the fully connected layers) of VGG16 network, (III) classifying EGFR mutation status according to the extracted features with a logistic regression model. In the experiments, the dataset contains 83 Chest CT series collecting from patients with non-small-cell lung cancer, half of whom are positive for a mutation in EGFR. The whole dataset was divided into two splits for training and testing with 66 CT series and 17 CT series respectively. Our pipeline achieves AUC of 0.725 (±0.009) when running a five-fold cross validation on training dataset and AUC of 0.75 on testing dataset, which validates the efficacy and generalizability of our approach and shows potential usage of non-invasive medical image analysis in detecting EGFR mutation status.

Citation Download Citation

Lin Tian and Rong Yuan "An automatic end-to-end pipeline for CT image-based EGFR mutation status classification", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492Q (15 March 2019); https://doi.org/10.1117/12.2512465

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