Radiomics: the bridge between medical imaging and personalized medicine
By Philippe Lambin, R. Leijenaar, T. Deist, J. Peerlings, E. D. Jong, J. V. Timmeren, S. Sanduleanu, R. Larue, A. Even, A. Jochems and 10 more. Nature Reviews Clinical Oncology, 2017
4,912 citations recorded by the source corpus.
Subfields: Oncology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/f9b5c08711b30103ffc7ba144d4f590bbd9b065b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Oncology
In Radiology, Nuclear Medicine and Imaging
- Hugo J. W. L. Aerts
- Philippe Lambin
- I. E. El Naqa
- Ronald Boellaard
- Fabian Isensee
- Klaus Hermann Maier-Hein
- Christos Davatzikos
- Robert James Gillies
Citations in the corpus
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, background): “The field currently grapples with a lack of standardisation and validation that are quintessential for ensuring reliable and reproducible outcomes [61,62].”
- The TRIPOD-LLM reporting guideline for studies using large language models (2025, neutral): “https://doi.org/10.1038/s41591-024-03425-5 the relevant components of TRIPOD-LLM statement items to report multimodal LLMs. Users may also refer to methodological guides from multiple AI fields, such as radiomics 48,49 , to inform their reporting.”
- Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. (2020, background): “A radiomics quality score has been proposed to assess the quality of radiomics studies (9).”
- A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, background): “In the last 5 years, an exponential increase in the number of radiomics publications has occurred, with a widening number of applications in many subdisciplines of radiology and radiation oncology (55, 56).”
- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. (2016, positive): “We defined a general radiomics image processing scheme based on descriptions in the literature (3,6,17,20).”
- Artificial intelligence and machine learning for medical imaging: A technology review. (2021, neutral): “An example of this strategy is the field of radiomics [104,105], where “ -omics-like ” features are extracted from radiological images in order to predict some indicator of interest like a disease grade or a patient ’ s survival.”
- Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, background): “Because of the rapid growth of this area, numerous published radiomics investigations lack standardised evaluation of both the scientific integrity and the clinical relevance [22].”
- Introduction to Radiomics (2020, background): “(48) developed a modality-independent radiomics quality score based on 16 criteria that carry different weights; a maximum of 36 points can be achieved.”
- Radiomics: the facts and the challenges of image analysis (2018, background): “Radiomics ’ analyses can be performed in tumour regions, metastatic lesions, as well as in normal tissues [2].”