Abstract
Objective The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases. Methods Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients. Results The whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94–0.98), 0.80 (95% CI: 0.72–0.88), and 0.73 (95% CI: 0.63–0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71). Conclusion In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.
Highlights
Lung cancer is the leading cause of cancer-related deaths and the second most commonly diagnosed cancer around the world, with around 1.8 million deaths and 2.2 million new cancer cases in 2020 [1]
Immune checkpoint inhibitors (ICIs) targeted to the programmed death ligand-1 (PD-L1) expressed by tumor cells would contribute to prolonged overall survival (OS) in PD-L1-positive patients with advanced Non-small-cell lung cancer (NSCLC) [6, 7]. erefore, it is extremely essential to identify the genetic status of patients in the era of precision medicine
We proposed a new approach to predict epidermal growth factor receptor (EGFR) mutation and PD-L1 expression status in NSCLC patients based on deep learning technology and selected features to build a prognostic model. is noninvasive and easy-to-use method would assist clinicians in making treatment decisions for patients
Summary
Lung cancer is the leading cause of cancer-related deaths and the second most commonly diagnosed cancer around the world, with around 1.8 million deaths and 2.2 million new cancer cases in 2020 [1]. Non-small-cell lung cancer (NSCLC) is the most common subtype of lung cancer, and the 5-year survival rate is less than 20%. Tyrosine kinase inhibitors (TKIs) targeted to epidermal growth factor receptor (EGFR) could lead to extend progression-free survival (PFS) compared with conventional chemotherapy in EGFR-mutated NSCLC patients [4, 5]. Immune checkpoint inhibitors (ICIs) targeted to the programmed death ligand-1 (PD-L1) expressed by tumor cells would contribute to prolonged overall survival (OS) in PD-L1-positive patients with advanced NSCLC [6, 7]. The common methods to obtain these tissue specimens, such as surgery or biopsy, are invasive, expensive, and slow, and tumor tissue varies in regard to time and space. Erefore, a noninvasive, convenient, and efficient method to predict genetic status is of imminent need Other limitations including, but not limited to, the difficulty to obtain materials, the potential requirement for a secondary biopsy, and poor DNA quality can delay subsequent treatment decisions [8, 9]. erefore, a noninvasive, convenient, and efficient method to predict genetic status is of imminent need
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