ArticleDer Radiologe2018
Machine learning: from radiomics to discovery and routine.
Article in Der Radiologe, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Texture-Based Radiomic SD-OCT Features Associated With Response to Anti-VEGF Therapy in a Phase III Neovascular AMD Clinical Trial.Translational vision science & technology · 2024Trial
- Noninvasive prediction of lymph-vascular space invasion in cervical cancer based on ultrasomics nomogram.BMC cancer · 2026Article
- Placental T2WI MRI-based radiomics-clinical nomogram predicts suspicious placenta accreta spectrum in patients with placenta previa.BMC medical imaging · 2024Article
- Predictive modeling and web-based tool for cervical cancer risk assessment: A comparative study of machine learning models.MethodsX · 2024Article
- Article
- Optimizing evaluation of endometrial receptivity in recurrent pregnancy loss: a preliminary investigation integrating radiomics from multimodal ultrasound via machine learning.Frontiers in endocrinology · 2024Article
- An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer.Frontiers in oncology · 2024Article
- Enhancing Prediction of Myocardial Recovery After Coronary Revascularization: Integrating Radiomics from Myocardial Contrast Echocardiography with Machine Learning.International journal of general medicine · 2024Article
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.Biomolecules · 2023Article
- Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think?International journal of environmental research and public health · 2023Article
- OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration.Ophthalmology science · 2022Article
- A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- [Tobacco smoke-related diseases].Radiologie (Heidelberg, Germany) · 2022Article
- Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [Cancers · 2022Article
- Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.European radiology · 2022Article
- Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI.Scientific reports · 2022Article
- CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network.Abdominal radiology (New York) · 2022Article
- AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models.Journal of biotechnology and biomedicine · 2022Article
- Applying artificial intelligence for cancer immunotherapy.Acta pharmaceutica Sinica. B · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology.
Indexed as
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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.