ReviewRadiological physics and technology2018
Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis.
Review in Radiological physics and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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Who cites it
11 citing papers in PubMed.
- Radiomic Parameters for the Evaluation of Response to Treatment in Metastatic Colorectal Cancer Patients with Liver Metastasis: Findings from the CAVE-GOIM mCRC Phase 2 Trial.Clinical drug investigation · 2024Trial
- Non-invasive prediction of the secondary enucleation risk in uveal melanoma based on pretreatment CT and MRI prior to stereotactic radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Noninvasive machine-learning models for the detection of lesion-specific ischemia in patients with stable angina with intermediate stenosis severity on coronary CT angiography.Physical and engineering sciences in medicine · 2025Article
- Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review.Frontiers in oncology · 2025Review
- Topology-based radiomic features for prediction of parotid gland cancer malignancy grade in magnetic resonance images.Magma (New York, N.Y.) · 2023Article
- Assessment of brain cancer atlas maps with multimodal imaging features.Journal of translational medicine · 2023Review
- Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma.Physical and engineering sciences in medicine · 2023Article
- Radiomics and Digital Image Texture Analysis in Oncology (Review).Sovremennye tekhnologii v meditsine · 2021Review
- Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features.Journal of personalized medicine · 2020Article
- Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients.Scientific reports · 2020Article
- Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role.BJR open · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided diagnosis (CAD) is a field that is essentially based on pattern recognition that improves the accuracy of a diagnosis made by a physician who takes into account the computer's "opinion" derived from the quantitative analysis of radiological images. Radiomics is a field based on data science that massively and comprehensively analyzes a large number of medical images to extract a large number of phenotypic features reflecting disease traits, and explores the associations between the features and patients' prognoses for precision medicine. According to the definitions for both, you may think that radiomics is not a paraphrase of CAD, but you may also think that these definitions are "image manipulation". However, there are common and different features between the two fields. This review paper elaborates on these common and different features and introduces the potential of radiomics for cancer diagnosis and treatment by comparing it with CAD.
Indexed as
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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.