Observational studyEuropean journal of nuclear medicine and molecular imaging2021
Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer.
Observational study in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 5 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 5 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
- Pooled it
- Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis.Frontiers in public health · 2022Pooled it
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Deep learning based on multiparametric MRI for differentiating benign from malignant PI-RADS 3 prostate lesions: a dual-center study.Scientific reports · 2026Article
- Systematic review and meta-analysis of AI in lung cancer metastasis imaging for diagnosis and prognosis.NPJ digital medicine · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Integrated nomogram for predicting intracranial progression-free survival with EGFR-TKI combined with cranial radiotherapy: a multicenter study.European radiology · 2026Article
- Translating Molecular Subtypes into Cost-Effective Radiogenomic Biomarkers for Prognosis of Colorectal Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting PD-L1 expression in advanced EGFR-mutant lung adenocarcinoma patients using NECT, CECT radiomics and clinical features.BMC cancer · 2026Article
- Could a multimodal fusion model integrating CT radiomics and systemic inflammatory markers improve preoperative risk stratification of parotid masses? A retrospective exploratory study.Frontiers in immunology · 2026Article
- Proteome-wide Mendelian randomization and colocalization analysis uncovers druggable targets for lung cancer across multiple phenotypes and complications.Discover oncology · 2025Article
- Advancing Non-Small-Cell Lung Cancer Management Through Multi-Omics Integration: Insights from Genomics, Metabolomics, and Radiomics.Diagnostics (Basel, Switzerland) · 2025Review
- RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning algorithms for predicting malignancy grades of lung adenocarcinoma and guiding treatments: CT radiomics-based comparisons.Journal of thoracic disease · 2025Article
- Computed Tomography-Based Radiomics and Genomics Analyses for Survival Prediction of Stage III Unresectable Non-Small Cell Lung Cancer Treated With Definitive Chemoradiotherapy and Immunotherapy.Molecular carcinogenesis · 2025Article
11 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveThe objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).
methodsIn this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.
resultsStandardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87.
conclusionsRadiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.
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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.