ArticleThoracic cancer2019
Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT-based radiomics signature.
Article in Thoracic cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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31 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- 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
- Single and multi-site CT-based radiogenomics analysis of metastatic lung adenocarcinoma and correlations with outcome.European radiology · 2026Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Bioinformatics analysis of DLGAP5 as a biomarker in lung adenocarcinoma: implications for immune infiltration and targeted therapy.Discover oncology · 2025Article
- Prediction of EGFR-TP53 genes co-mutations in patients with lung adenocarcinoma (LUAD) byClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Endoscopic ultrasound-based radiomics for predicting pathologic upgrade in esophageal low-grade intraepithelial neoplasia.Surgical endoscopy · 2025Article
- Establishing a predictive model for tumor mutation burden status based onTranslational lung cancer research · 2024Article
- Article
- CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer.BMC medical imaging · 2024Article
- External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.Journal of digital imaging · 2023Article
- CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients.Cancers · 2023Article
- Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians.Frontiers in immunology · 2023Review
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Predicting Tumor Mutation Burden andJournal of personalized medicine · 2022Article
- Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results.Cancers · 2022Review
- Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma.Frontiers in oncology · 2022Article
- ZNF143 Expression is Associated with COPD and Tumor Microenvironment in Non-Small Cell Lung Cancer.International journal of chronic obstructive pulmonary disease · 2022Article
- Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study.BMC cancer · 2021Article
- Review
- Simultaneous Identification ofCancers · 2021Article
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Authors and funding
17 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTumor mutation burden (TMB) is an important determinant and biomarker for response of targeted therapy and prognosis in patients with lung cancer. The present study aimed to determine whether radiomics signature could non-invasively predict the TMB status and driver mutations in patients with resectable early stage lung adenocarcinoma (LUAD).
methodsA total of 61pulmonary nodules (PNs) from 51 patients post-operatively diagnosed LUAD were enrolled for analysis. Two datasets were divided according to two-thirds of patients from different commercial Comprehensive Genomic Profiling (CGP) panels: a training cohort including 41 PNs and a testing cohort including rest 20PNs. We sequenced all tumor specimens and paired blood cells using next generation sequencing (NGS), so as to detect TMB status and somatic mutations. We collected 718 quantitative 3D radiomics features extracted from segmented volumes of each PNs and 78 clinical and pathological features retrieved from medical records as well. Support vector machine methods were performed to establish the predictive model.
resultsWe established an efficient fusion-positive tumor prediction model that predicts TMB status and EGFR/TP53 mutations of early stage LUAD. The radiomics signature yielded a median AUC value of 0.606, 0.604, and 0.586 respectively. Combining radiomics with the clinical information can further improve the prediction performance, which the median AUC values are 0.671 for TMB, 0.697 and 0.656 for EGFR/TP53 respectively.
conclusionIt is feasible and effective to facilitate TMB and somatic driver mutations prediction by using the radiomics signature and NGS data in early stage LUAD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.