ReviewSeminars in nuclear medicine2020
A Role for FDG PET Radiomics in Personalized Medicine?
Review in Seminars in nuclear medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
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
13 citing papers in PubMed, 22 citations in OpenAlex.
- Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Review on radiomic analysis inCancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics.Yonsei medical journal · 2023Article
- The Role of Molecular Imaging in Personalized Medicine.Journal of personalized medicine · 2023Review
- Principal component analysis of texture features derived from FDG PET images of melanoma lesions.EJNMMI physics · 2022Article
- Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase.European radiology · 2022Article
- Comparison of FDG PET/CT and Bone Marrow Biopsy Results in Patients with Diffuse Large B Cell Lymphoma with Subgroup Analysis of PET Radiomics.Diagnostics (Basel, Switzerland) · 2022Article
- Application of dual-stream 3D convolutional neural network based onEJNMMI physics · 2021Article
- Role ofJournal of clinical medicine · 2021Article
- [European journal of nuclear medicine and molecular imaging · 2021Article
- Quantitative FDG PET Assessment for Oncology Therapy.Cancers · 2021Review
- Value of Shape and Texture Features fromDiagnostics (Basel, Switzerland) · 2020Article
- Radiomics in PET/CT and HRCT for systemic sclerosis-associated interstitial lung disease: breakthroughs and future directions.Radiologia brasileiraArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using
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What 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.