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Radiomics and Machine Learning in Medical Imaging
Research Guide
What is Radiomics and Machine Learning in Medical Imaging?
Radiomics and Machine Learning in Medical Imaging is the quantitative analysis of medical images to extract advanced features, combined with machine learning for predictive modeling, particularly in cancer imaging and precision medicine.
This field encompasses 130,046 works focused on radiomics feature extraction from medical images and machine learning applications for tumor heterogeneity assessment. Key methods include texture analysis and high-throughput quantitative feature extraction from datasets. Growth data over the past five years is not available in the provided records.
Topic Hierarchy
Research Sub-Topics
Radiomics Feature Extraction
Researchers develop algorithms for extracting high-throughput quantitative features from medical images including first-order statistics and texture metrics. Studies focus on reproducibility through image normalization and segmentation standardization.
Machine Learning in Radiomics Prediction
This sub-topic covers radiomics-based ML models for outcome prediction including survival and treatment response. Research addresses overfitting through cross-validation and dimensionality reduction techniques.
Tumor Heterogeneity via Radiomics
Studies use radiomics texture analysis to quantify intra-tumoral heterogeneity correlating with genomics and outcomes. Researchers validate imaging heterogeneity as hypoxia and genetic instability surrogate.
Radiomics in Cancer Precision Medicine
Investigations integrate radiomics with genomics for patient stratification and therapy selection. Research explores radiogenomics associations between imaging phenotypes and molecular pathways.
Radiomics Reproducibility and Standardization
Researchers tackle inter-platform variability, scanner effects, and protocol standardization in radiomics. Studies develop IBSI standards and ComBat harmonization for multicenter validation.
Why It Matters
Radiomics and machine learning enable precise tumor characterization in cancer imaging, supporting precision medicine applications. For instance, low-dose CT screening reduced lung-cancer mortality, as shown in "Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening" (2011) with results from the National Lung Screening Trial (ClinicalTrials.gov NCT00047385). In glioma diagnosis, machine learning-driven radiomics on ¹⁸F-FDG PET provides systematic review and meta-analysis for improved classification, as detailed in recent preprints. Tools like pyradiomics facilitate reproducible feature extraction from 2D and 3D images, aiding clinical translation in radiation therapy and pathology, referenced in news on AI integration for prognosis and treatment response prediction.
Reading Guide
Where to Start
"Radiomics: Images Are More than Pictures, They Are Data" (Gillies et al., 2015) first, as it provides a foundational explanation of radiomics as quantitative data extraction from medical images, essential before machine learning applications.
Key Papers Explained
"Radiomics: Images Are More than Pictures, They Are Data" (Gillies et al., 2015) establishes quantitative feature extraction, built upon by "A survey on deep learning in medical image analysis" (Litjens et al., 2017) which reviews deep learning for image analysis including segmentation. "3D Slicer as an image computing platform for the Quantitative Imaging Network" (Fedorov et al., 2012) supports these with tools for quantitative imaging, while "UNet++: A Nested U-Net Architecture for Medical Image Segmentation" (Zhou et al., 2018) advances segmentation critical for radiomics pipelines. "High-performance medicine: the convergence of human and artificial intelligence" (Topol, 2018) connects to broader AI applications.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent preprints focus on reproducibility with "Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine" and "Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines." Guidelines in "Robust radiomics: a review of guidelines for radiomics in medical imaging" address robust pipelines. News highlights machine learning-based MRI radiomics for IL18 prediction in gliomas and FDA approvals for computational pathology.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | New response evaluation criteria in solid tumours: Revised REC... | 2008 | European Journal of Ca... | 28.3K | ✕ |
| 2 | TNM Classification of Malignant Tumours | 1987 | — | 16.1K | ✕ |
| 3 | A survey on deep learning in medical image analysis | 2017 | Medical Image Analysis | 13.1K | ✓ |
| 4 | Reduced Lung-Cancer Mortality with Low-Dose Computed Tomograph... | 2011 | New England Journal of... | 10.6K | ✓ |
| 5 | The American Joint Committee on Cancer: the 7th Edition of the... | 2010 | Annals of Surgical Onc... | 9.1K | ✓ |
| 6 | 3D Slicer as an image computing platform for the Quantitative ... | 2012 | Magnetic Resonance Ima... | 8.3K | ✓ |
| 7 | UNet++: A Nested U-Net Architecture for Medical Image Segmenta... | 2018 | Lecture notes in compu... | 8.2K | ✓ |
| 8 | Radiomics: Images Are More than Pictures, They Are Data | 2015 | Radiology | 7.8K | ✕ |
| 9 | QuPath: Open source software for digital pathology image analysis | 2017 | Scientific Reports | 7.8K | ✓ |
| 10 | High-performance medicine: the convergence of human and artifi... | 2018 | Nature Medicine | 7.0K | ✕ |
In the News
News | MAIA Lab | UT Southwestern, Dallas, Texas
November 2019 ## "Radiomics and Artificial Intelligence in Radiation Therapy" at the 2019 RRS Meeting
Machine learning-based MRI radiomics predict IL18 ...
noninvasive MRI-based radiomics machine learning framework to predict IL18 expression status and prognostic outcomes in low-grade glioma patients. Finally, external validation was performed through...
What Is the Role of Machine Learning in Oncology Today?
In diagnostics, machine learning models are increasingly used to integrate multimodal data in histopathology (pathomics) and radiology (radiomics). This year, the FDA approved the first computation...
Artificial Intelligence, Machine Learning, and Deep Learning
* natural-language processing and understanding * medical decision-making * predictive modeling * computer vision * robotic and image guided surgery * personalized imaging and treatment * drug disc...
Application of Artificial Intelligence in Medical Image Analysis
clinical practice.・ Radiomics and AI integration: combining radiomics with AI methodologies to extract quantitative imaging features and improve disease characterization, prognosis, and treatment r...
Code & Tools
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c...
With this package we aim to establish a reference standard for Radiomic Analysis, and provide a tested and maintained open-source platform for easy...
A fully automated pipeline for**radiomics feature extraction**,**selection**,**classification**, and**analysis**.
Please cite it if you're using the framework for your research. ## About The easiest tool for experimenting with radiomics features. ### Topics me...
# Medical Image Radiomics Processor MIRP is a python package for quantitative analysis of medical images. It focuses on processing images for int...
Recent Preprints
Machine Learning–Driven Radiomics for Glioma Diagnosis
Our new paper, “Machine Learning–Driven Radiomics on ¹⁸F-FDG PET for Glioma Diagnosis: A Systematic Review and Meta-Analysis,” is now published in Cancer Imaging.
Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine
Radiomics is a tool for medical imaging analysis that could have a relevant role in precision oncology by offering precise quantitative support for clinical decision-making. The Radiomics Quality S...
Robust radiomics: a review of guidelines for radiomics in medical imaging
**Introduction:** Radiomics aims to develop image-based biomarkers by combining quantitative analysis of medical images with artificial intelligence (AI) through a robust, reproducible pipeline. Sc...
Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines
As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a ...
Machine Learning–Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis
Radiomics has emerged as a transformative approach to overcome these challenges by extracting high-dimensional, quantitative features from medical images that characterize tumor intensity, texture,...
Latest Developments
Recent developments in radiomics and machine learning in medical imaging research as of February 2026 include a conceptual shift towards using imaging for prognostic and predictive purposes through quantitative pattern extraction, with advancements highlighted in 2026 (Open Medscience). Additionally, there is ongoing research integrating multimodal deep learning radiomics for traditional Chinese medicine classification, and new AI-driven tools are expected to become standard in imaging departments to streamline workflows (GeneOnline, Beekley Blog). Notably, the Radiomics Quality Score 2.0 has been introduced to improve clinical translation, and there are active efforts to standardize datasets and pipelines for reproducible radiomics machine learning (Nature, BMC Medical Imaging). Furthermore, specialized conferences and courses are focusing on AI applications, deep learning, foundation models, and synthetic data in radiomics (Imaging.org, AI4Imaging).
Sources
Frequently Asked Questions
What is radiomics in medical imaging?
Radiomics involves high-throughput extraction of quantitative features from medical images to characterize tumor intensity, texture, and shape. "Radiomics: Images Are More than Pictures, They Are Data" (Gillies et al., 2015) notes that advances in pattern recognition have facilitated this process from exponentially growing datasets. These features support machine learning models for predictive tasks in cancer imaging.
How does machine learning integrate with radiomics?
Machine learning algorithms process high-dimensional radiomics features for classification and predictive modeling. "A survey on deep learning in medical image analysis" (Litjens et al., 2017) reviews deep learning applications in medical image analysis, including segmentation relevant to radiomics pipelines. Recent preprints like "Machine Learning–Driven Radiomics for Glioma Diagnosis" combine these for PET-based glioma classification.
What tools support radiomics feature extraction?
Pyradiomics is an open-source Python package for extracting radiomics features from 2D and 3D images and binary masks. "3D Slicer as an image computing platform for the Quantitative Imaging Network" (Fedorov et al., 2012) provides an image computing platform supporting such quantitative analysis. These tools establish reference standards for reproducible radiomic analysis.
What are current applications in cancer imaging?
Applications include tumor staging and response evaluation, as in "New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)" (Eisenhauer et al., 2008) with 28,308 citations. Machine learning enhances segmentation in "UNet++: A Nested U-Net Architecture for Medical Image Segmentation" (Zhou et al., 2018). Preprints address glioma diagnosis and radiation therapy via radiomics.
What guidelines exist for robust radiomics studies?
The Radiomics Quality Score (RQS) 2.0 assesses study rigor for clinical translation in precision oncology. "Robust radiomics: a review of guidelines for radiomics in medical imaging" summarizes pipelines combining quantitative analysis with AI. These promote reproducibility in medical imaging research.
What is the state of reproducibility in radiomics?
Challenges include reproducibility and accessibility, addressed by "Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines." This provides datasets and protocols for feature extraction, selection, and classification. Forks like bhklab/pyradiomics-fork maintain open-source standards.
Open Research Questions
- ? How can radiomics features be standardized across multi-center datasets to improve reproducibility in machine learning models?
- ? What pipelines best integrate deep learning segmentation with radiomics for real-time clinical predictive modeling?
- ? Which radiomics features most accurately predict treatment response beyond RECIST criteria in solid tumors?
- ? How do guidelines like RQS 2.0 influence the clinical translation of radiomics in precision oncology?
- ? What effects do imaging protocols have on the robustness of machine learning-driven radiomics for glioma diagnosis?
Recent Trends
Preprints from the last six months emphasize reproducibility, with "Radiomics Quality Score 2.0" updating RQS for clinical translation and "Open-radiomics" (2025-08-04) providing standardized datasets for machine learning pipelines. "Machine Learning–Driven radiomics on 18F-FDG PET for glioma diagnosis" (2025-08-26) offers meta-analysis for PET applications.
2025News covers AI in radiation therapy (2019 RRS Meeting) and MRI radiomics for glioma prognosis, alongside FDA approvals for pathomics-radiomics integration.
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