ReviewThe British journal of radiology2018
A review on radiomics and the future of theranostics for patient selection in precision medicine.
Review 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 35 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive performance of MRI and CT radiomics in predicting the response to induction chemotherapy in nasopharyngeal carcinoma: a network meta-analysis.Frontiers in oncology · 2025Pooled it
- MRI in Oral Tongue Squamous Cell Carcinoma: A Radiomic Approach in the Local Recurrence Evaluation.Current oncology (Toronto, Ont.) · 2025Article
- Circulating Tumor Cells in Cancer Diagnosis, Therapy, and Theranostics Applications: An Overview of Emerging Materials and Technologies.Current pharmaceutical design · 2025Review
- Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy.European radiology · 2024Article
- The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study.La Radiologia medica · 2024Article
- Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.BMC cardiovascular disorders · 2024Article
- Application of deep learning and feature selection technique on external root resorption identification on CBCT images.BMC oral health · 2024Article
- Nanotechnology-based delivery systems to overcome drug resistance in cancer.Medical review (2021) · 2024Review
- Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment.Frontiers in neurology · 2024Article
- Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.Nature communications · 2023Article
- Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques.Cancers · 2023Article
- Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique.Journal of digital imaging · 2023Article
- MulticentricFrontiers in nuclear medicine · 2023Article
- A first look into radiomics application in testicular imaging: A systematic review.Frontiers in radiology · 2023Review
- Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC.Scientific reports · 2022Article
- Blockchain in radiology research and clinical practice: current trends and future directions.La Radiologia medica · 2022Review
- High-dimensional role of AI and machine learning in cancer research.British journal of cancer · 2022Review
- A review on advances inInsights into imaging · 2022Review
- Application of deep learning upon spinal radiographs to predict progression in adolescent idiopathic scoliosis at first clinic visit.EClinicalMedicine · 2021Article
- Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics.Insights into imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing complexity and volume of clinical data and the associated decision-making processes in oncology promote the advent of precision medicine. Precision (or personalised) medicine describes preventive and/or treatment procedures that take individual patient variability into account when proscribing treatment, and has been hindered in the past by the strict requirements of accurate, robust, repeatable and preferably non-invasive biomarkers to stratify both the patient and the disease. In oncology, tumour subtypes are traditionally measured through repeated invasive biopsies, which are taxing for the patient and are cost and labour intensive. Quantitative analysis of routine clinical imaging provides an opportunity to capture tumour heterogeneity non-invasively, cost-effectively and on large scale. In current clinical practice radiological images are qualitatively analysed by expert radiologists whose interpretation is known to suffer from inter- and intra-operator variability. Radiomics, the high-throughput mining of image features from medical images, provides a quantitative and robust method to assess tumour heterogeneity, and radiomics-based signatures provide a powerful tool for precision medicine in cancer treatment. This study aims to provide an overview of the current state of radiomics as a precision medicine decision support tool. We first provide an overview of the requirements and challenges radiomics currently faces in being incorporated as a tool for precision medicine, followed by an outline of radiomics' current applications in the treatment of various types of cancer. We finish with a discussion of possible future advances that can further develop radiomics as a precision medicine tool.
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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.