ArticleScientific data2018
A radiogenomic dataset of non-small cell lung cancer.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers, 3 of them syntheses that pooled it.
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Who cites it
146 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Insights into pet-based radiogenomics in oncology: an updated systematic review.European journal of nuclear medicine and molecular imaging · 2025Pooled it
- Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Association of radiomic features with genomic signatures in thyroid cancer: a systematic review.Journal of translational medicine · 2024Pooled it
- CT-based Intratumoral Heterogeneity Score for Preoperative Prediction of Lymphovascular Invasion and Prognosis in Lung Adenocarcinoma: A Multicenter Validation and Radiogenomic Interpretation.Annals of surgical oncology · 2026Article
- Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.European radiology · 2026Article
- Transformer-Based PET/CT Fusion Enables Preoperative Prediction and Prognostic Stratification of Spread Through Air Spaces in Lung Adenocarcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma.Molecular oncology · 2026Article
- Development and external validation of a FDG PET-based radiomics model predicting occult lymph node metastasis in non-small cell lung cancer patients.European journal of nuclear medicine and molecular imaging · 2026Article
- Radiomics dataset from chest CT of clinically healthy adults.Data in brief · 2026Article
- A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer.Scientific reports · 2026Article
- A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.Radiology. Imaging cancer · 2026Article
- Independent Testing of Published CT Models for PD-L1 Status in Non-Small Cell Lung Cancer.Radiology · 2026Article
- Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and diagnosis of lung nodules.Japanese journal of radiology · 2026Article
- Closed loop text guided framework for lung cancer lesion segmentation and quantification.NPJ digital medicine · 2026Article
- Quantification of PET activation in adipose tissue from non-contrast CT scans.NPJ digital medicine · 2026Article
- A CT imaging-based deep learning model for predicting EGFR and KRAS mutations in non-small cell lung cancer: toward personalized treatment approaches.Translational lung cancer research · 2026Article
- Non-invasive prediction of invasive lung adenocarcinoma and high-risk histopathological characteristics in resectable early-stage adenocarcinoma by [18F]FDG PET/CT radiomics-based machine learning models: a prospective cohort study.International journal of surgery (London, England) · 2026Observational
- Non-invasive prediction of prognostic immune subtypes in lung adenocarcinoma using PET/CT-based radiomics.Frontiers in oncology · 2026Article
- An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis.Quantitative imaging in medicine and surgery · 2025Article
- MetaPredictomics: A Comprehensive Approach to Predict Postsurgical Non-Small Cell Lung Cancer Recurrence Using Clinicopathologic, Radiomics, and Organomics Data.Clinical nuclear medicine · 2025Article
86 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
Funding
Abstract
Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
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