Radiomics and Machine Learning in Medical Imaging
Radiomics and Machine Learning in Medical Imaging is a research topic within Radiology, Nuclear Medicine and Imaging. Science Explorer counts 71k research works in it since 1950. 16.1% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the application of radiomics, a quantitative analysis of medical images, particularly in the context of cancer imaging and precision medicine. It explores the extraction of advanced features from medical images, the use of machine learning for predictive modeling, and the assessment of tumor heterogeneity through texture analysis.
- Radiomics
- Medical Imaging
- Quantitative Analysis
- Feature Extraction
- Tumor Heterogeneity
- Machine Learning
- Cancer Imaging
- Predictive Modeling
- Texture Analysis
- Precision Medicine
- Research works
- 71k fractional, since 1950
- In the world top 10%
- 11k per year above
- Top-10% rate
- 16.1% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +88% the tick is no change
This ranks research output and citation impact. It says nothing about the quality of diagnosis, treatment or care.
Which countries lead Radiomics and Machine Learning in Medical Imaging research?
By volume, China and the United States publish the most (6.7k and 4.7k works in 2022–2025).
By volume, 2022–2025
- 1 China 6.7k works
- 2 United States 4.7k works
- 3 India 2.4k works
- 4 United Kingdom 988 works
- 5 Germany 965 works
- 6 Italy 827 works
- 7 Japan 749 works
- 8 South Korea 669 works
- 9 France 502 works
- 10 Canada 498 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Radiomics and Machine Learning in Medical Imaging research?
By volume in 2022–2025, The University of Texas MD Anderson Cancer Center publishes the most Radiomics and Machine Learning in Medical Imaging research, followed by Sun Yat-sen University and Chinese Academy of Medical Sciences & Peking Union Medical College.
By volume, 2022–2025
- 1 The University of Texas MD Anderson Cancer Center United States 120 works
- 2 Sun Yat-sen University China 115 works
- 3 Chinese Academy of Medical Sciences & Peking Union Medical College China 108 works
- 4 Memorial Sloan Kettering Cancer Center United States 100 works
- 5 Shanghai Jiao Tong University China 84 works
- 6 Sichuan University China 79 works
- 7 Stanford University United States 78 works
- 8 Saveetha University India 78 works
- 9 Vellore Institute of Technology University India 77 works
- 10 SRM Institute of Science and Technology India 75 works
Who are the leading researchers in Radiomics and Machine Learning in Medical Imaging?
The most-cited researchers publishing on Radiomics and Machine Learning in Medical Imaging include Patrick M. Bossuyt, Xiaogang Wang and Alan Yuille.
- 1 Patrick M. Bossuyt 14k citations
- 2 Xiaogang Wang 13k citations
- 3 Alan Yuille 8.4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Radiomics and Machine Learning in Medical Imaging research done?
The largest centres of Radiomics and Machine Learning in Medical Imaging research in 2022–2025 are Beijing (China), Shanghai (China), Guangzhou (China) and Seoul (South Korea). Among places with at least 20 works in it, it is an unusually large share of all research in Plainview, Rozzano and Villejuif.
Largest cities, 2022–2025
Where it is the local speciality
- PlainviewUS · 24.9 works13×
- RozzanoIT · 35.6 works9.9×
- VillejuifFR · 20.4 works6.5×
Location quotient: how much more of its research is in Radiomics and Machine Learning in Medical Imaging than the world average.
Where is the best place to study Radiomics and Machine Learning in Medical Imaging?
Among universities, judged by research, Shanghai Medical College of Fudan University, Humanitas University and Southern Medical University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Shanghai Medical College of Fudan University China | 56.8 | 33.3% | 11.2× | 21 | +75.1% |
| 2 | Humanitas University Italy | 56.4 | 19.1% | 9.4× | 18 | +628.7% |
| 3 | Southern Medical University China | 52.2 | 26.4% | 6.6× | 56 | +74.0% |
| 4 | Princess Nourah bint Abdulrahman University Saudi Arabia | 51.0 | 58.8% | 2.2× | 14 | — |
| 5 | Sun Yat-sen University China | 50.9 | 25.9% | 3.0× | 115 | +103.9% |
| 6 | Ulsan College South Korea | 50.6 | 28.4% | 7.0× | 16 | +177.2% |
| 7 | Sichuan University China | 50.5 | 26.7% | 1.9× | 80 | +196.4% |
| 8 | Chinese Academy of Medical Sciences & Peking Union Medical College China | 49.8 | 17.9% | 4.7× | 108 | +93.2% |
| 9 | Institute of Cancer Research United Kingdom | 48.9 | 21.5% | 15.1× | 12 | +71.3% |
| 10 | Shandong First Medical University China | 48.6 | 22.2% | 8.4× | 52 | -13.2% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Radiomics and Machine Learning in Medical Imaging research growing?
Output in 2018–2022 was 88% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Radiomics and Machine Learning in Medical Imaging.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.