ReviewAnnals of translational medicine2020
Radiomics: an overview in lung cancer management-a narrative review.
Review in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
19 citing papers in PubMed.
- A CT-based radiomics nomogram for the differentiation of pulmonary cystic echinococcosis from pulmonary abscess.Parasitology research · 2022Trial
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Predictive classification of lung cancer pathological based on PET/CT radiomics.Japanese journal of radiology · 2025Article
- Development and validation of multi-sequence magnetic resonance imaging radiomics models for predicting tumor response to radiotherapy in locally advanced non-small cell lung cancer.Journal of thoracic disease · 2025Article
- Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.Journal of translational medicine · 2024Observational
- Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Radiomics Features in Contrast-Enhanced and Nonenhanced Magnetic Resonance Imaging Images Are Associated With High Intracranial Aneurysmal Risk.Stroke (Hoboken, N.J.) · 2023Article
- Radiomic applications in upper gastrointestinal cancer surgery.Langenbeck's archives of surgery · 2023Review
- Multi-task learning-based histologic subtype classification of non-small cell lung cancer.La Radiologia medica · 2023Article
- Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images.Frontiers in oncology · 2023Article
- Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients.Annals of translational medicine · 2022Article
- IdentifyingQuantitative imaging in medicine and surgery · 2022Article
- Trends in Imaging Patterns of Bronchogenic Carcinoma: Reality or a Statistical Variation? A Single-Center Cross-Sectional Analysis of Outcomes.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2022Article
- Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review.Journal of thoracic disease · 2021Review
- Bibliometrics research on radiomics of lung cancer.Translational cancer research · 2021Article
- Precision Medicine and Melanoma: Multi-Omics Approaches to Monitoring the Immunotherapy Response.International journal of molecular sciences · 2021Review
- Alantolactone: A Natural Plant Extract as a Potential Therapeutic Agent for Cancer.Frontiers in pharmacology · 2021Review
- Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer byFrontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is a novel approach for optimizing the analysis massive data from medical images to provide auxiliary guidance in clinical issues. Quantitative feature extraction is one of the critical steps of radiomics. The association between radiomics features and the clinicopathological information of diseases can be identified by several statistics methods. For instance, although significant progress has been made in the field of lung cancer, too many questions remain, especially for the individualized decisions. Radiomics offers a new tool to encode the characteristics of lung cancer which is the leading cause of cancer-related deaths worldwide. Here, we reviewed the workflow and clinical utility of radiomics in lung cancer management, including pulmonary nodules detection, classification, histopathology and genetics evaluation, clinical staging, therapy response, and prognosis prediction. Most of these studies showed positive results, indicating the potential value of radiomics in clinical practice. The implementation of radiomics is both feasible and invaluable, and has aided clinicians in ascertaining the nature of a disease with greater precision. However, it should be noted that radiomics in its current state cannot completely replace the work of therapists or tissue examination. The potential future trends of this modality were also remarked. More efforts are needed to overcome the limitations identified above in order to facilitate the widespread application of radiomics in the reasonably near future.
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Registered trials
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.