ReviewDer Radiologe2020
[A primer on radiomics].
Review in Der Radiologe, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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
8 citing papers in PubMed.
- Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.Insights into imaging · 2024Article
- Applicability of the CT Radiomics of Skeletal Muscle and Machine Learning for the Detection of Sarcopenia and Prognostic Assessment of Disease Progression in Patients with Gastric and Esophageal Tumors.Diagnostics (Basel, Switzerland) · 2024Article
- Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.Japanese journal of radiology · 2023Review
- Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI.Medical & biological engineering & computing · 2022Article
- [Potential of radiomics and artificial intelligence in myeloma imaging : Development of automatic, comprehensive, objective skeletal analyses from whole-body imaging data].Der Radiologe · 2022Review
- Radiomics in radiation oncology for gynecological malignancies: a review of literature.The British journal of radiology · 2021Review
- [Artificial intelligence in hybrid imaging].Der Radiologe · 2020Review
- [A primer on machine learning].Der Radiologe · 2020Review
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
clinical issueThe reproducible and exhaustive extraction of information from radiological images is a central task in the practice of radiology. Dynamic developments in the fields of artificial intelligence (AI) and machine learning are introducing new methods for this task. Radiomics is one such method and offers new opportunities and challenges for the future of radiology. METHODOLOGICAL INNOVATIONS: Radiomics describes the quantitative evaluation, interpretation, and clinical assessment of imaging markers in radiological data. Components of a radiomics analysis are data acquisition, data preprocessing, data management, segmentation of regions of interest, computation and selection of imaging markers, as well as the development of a radiomics model used for diagnosis and prognosis. This article explains these components and aims at providing an introduction to the field of radiomics while highlighting existing limitations. MATERIALS AND
methodsThis article is based on a selective literature search with the PubMed search engine. ASSESSMENT: Even though radiomics applications have yet to arrive in routine clinical practice, the quantification of radiological data in terms of radiomics is underway and will increase in the future. This holds the potential for lasting change in the discipline of radiology. Through the successful extraction and interpretation of all the information encoded in radiological images the next step in the direction of a more personalized, future-oriented form of medicine can be taken.
Indexed as
Identifiers
31820014What OpenQuestion holds
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.