Deep learning for preoperative prediction of the EGFR mutation and subtypes based on the MRI image of spinal metastasis from primary NSCLC. (January 2023)

• Record Type: Journal Article
• Authors: Jiang, Tao; Sun, Xinyan; Dong, Yue; Guo, Wei; Wang, Hongbo; Yue, Zhibin; Luo, Yahong; Jiang, Xiran
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: Deep learning -- EGFR -- Metastasis -- NSCLC; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104084
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: To our knowledge, this is the first attempt to propose a deep learning model for predicting the EGFR mutation and subtypes based on the bone metastasis originated from primary NSCLC. A CM-EfNet was proposed by integrating the convolutional block attention module (CBAM) and multi-resolution feature fusion mechanism (MFM) with EfficientNet v2 to enhance the activation response of specific areas and obtain multi-dimension semantic information of bone MRI images. The CM-EfNet was externally validated, and showed good performance to assist clinicians in improving their prediction capabilities on the EGFR mutation and mutation subtypes. Abstract: Preoperative prediction of EGFR mutation status and subtypes is essential to choose appropriate treatment strategies and increase the survival of NSCLC patients. However, existing computer-aided predicting methods mainly focused on the primary tumour and rely on traditional handcrafted-based radiomics features and machine learning classifiers, which have inherent limitations due to huge computation complexity and low accuracy. This study made the first attempt to investigate an end-to-end deep learning technique for detecting EGFR mutations and subtypes based on bone metastasis originated from primary NSCLC. A CM-EfNet was proposed by integrating the convolutional block attention module (CBAM) and multi-resolution feature fusion mechanism (MFM) with EfficientNet v2. For detecting EGFR mutations, the proposed CM-EfNet achieved the highest AUCs of 0.851 and 0.764 in primary and external validation cohorts, respectively. For predicting EGFR mutation sites in exon 19 versus exon 21, the CM-EfNet also generated the highest AUCs of 0.711 and 0.687 in primary and external validation cohorts, respectively. Our research offers a potential non-invasive end-to-end imaging tool for preoperative prediction of EGFR mutations and mutation subtypes for metastatic NSCLCs.