ReviewEuropean journal of nuclear medicine and molecular imaging2021
Structural and functional radiomics for lung cancer.
Review 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 89 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
89 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024Pooled it
- Associations between CT characteristics of lung cancer lesions and histologic subtypes in lung cancer screening.European radiology · 2026Article
- Machine Learning-Driven Radiomics for an Early-Stage Predictive Model of Nodal Upstaging in Thoracic Oncology.Cancers · 2026Article
- Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.BMC pulmonary medicine · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Risk stratification of PD-L1 expression in non-small cell lung cancer: a predictive model for prognosis.Journal of thoracic disease · 2026Article
- Unsupervised radiomics-driven endotyping of chronic rhinosinusitis with nasal polyps: a multimodal characterization of CT imaging, clinical phenotypes, and proteomic profiling.BMC medical imaging · 2026Article
- Radiomics identifies distinct cortical bone texture alterations in patients with CKD using HR-pQCT.Bone research · 2026Article
- Habitat-based CT radiomics profiling spatial-temporal heterogeneity in resectable NSCLC predict pathological response to neoadjuvant chemoimmunotherapy: a multi-center study.NPJ precision oncology · 2026Article
- Prognostic value of a combined model integrating clinical and PET radiomics parameters in metastatic melanoma: A dual-center retrospective study.Annals of nuclear medicine · 2026Article
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- Prediction of ISS and R-ISS Stratification in Newly Diagnosed Multiple Myeloma Using Lumbar Spine MRI Radiomics Model: A Two-Center Multimodal Study.Journal of imaging informatics in medicine · 2026Article
- Combining computed tomography features and histogram analysis facilitates differentiation between solitary pulmonary invasive mucinous and non-mucinous adenocarcinomas.Frontiers in radiology · 2026Article
- Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Non-invasive Prediction of Lung Cancer Histological Differentiation via Radiomics and Multi-binary Classification ModelsCurrent medical imaging · 2026Article
- Machine learning-based quantitative prediction of spread through air spaces in primary lung adenocarcinoma using intratumoural heterogeneity scores.International journal of surgery (London, England) · 2025Article
- Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- A computed tomography-based deep learning radiomics for predicting the response to neoadjuvant chemotherapy combined with immunotherapy in patients with locally advanced esophageal cancer: a multicenter cohort study.Journal of thoracic disease · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.
methodsHere, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer.
conclusionThe major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."
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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.