ArticleJournal of oncology2021
Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images.
Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 5 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- TRANS: a prediction model for EGFR mutation status in NSCLC based on radiomics and clinical features.Respiratory research · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Prediction of epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion.Scientific reports · 2024Article
- Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer.European journal of radiology open · 2024Article
- EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC.Journal of imaging informatics in medicine · 2024Article
- A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?NPJ precision oncology · 2024Review
- A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.European radiology experimental · 2024Article
- Deep learning-based image analysis predicts PD-L1 status fromFrontiers in oncology · 2024Article
- Radiomics based onFrontiers in oncology · 2024Article
- Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features.Frontiers in oncology · 2024Article
- Quantifying innervation facilitated by deep learning in wound healing.Scientific reports · 2023Article
- Novel tools for early diagnosis and precision treatment based on artificial intelligence.Chinese medical journal pulmonary and critical care medicine · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe 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.
methodsEligible 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.
resultsThe whole cohort (
conclusionIn 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.
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