SynthesisThe British journal of radiology2018
Clinical applications of textural analysis in non-small cell lung cancer.
Synthesis in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 38 citations in OpenAlex.
- Explainable machine learning-based mortality prediction in critically ill patients with rheumatoid arthritis-associated lung disease: a radiomics and clinical data integration study.BMC medical imaging · 2026Article
- Image analysis:Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.Skeletal radiology · 2025Article
- An Enhanced CT-based Radiomics Model for Predicting the Anaplastic Lymphoma Kinase Mutation Status in Lung Adenocarcinoma.Current medical imaging · 2025Article
- Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions.Journal of imaging · 2024Review
- Machine Learning and Texture Analysis of [Biomedicines · 2024Article
- Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors.European journal of medical research · 2023Article
- Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules.Quantitative imaging in medicine and surgery · 2023Article
- Radiomics in Breast Imaging: Future Development.Journal of personalized medicine · 2023Article
- Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.European radiology · 2022Article
- Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment.Cancers · 2021Review
- CT based radiomic approach on first line pembrolizumab in lung cancer.Scientific reports · 2021Article
- Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT.Journal of applied clinical medical physics · 2021Article
- MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study.European radiology experimental · 2021Article
- Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis.Heliyon · 2020Article
- Radiomics: an overview in lung cancer management-a narrative review.Annals of translational medicine · 2020Review
- Computed tomography texture analysis of response to second-line nivolumab in metastatic non-small cell lung cancer.Lung cancer management · 2020Article
- A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma.Journal of medical systems · 2019Article
- Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan.BJR open · 2019Article
- Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.
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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.