Computational Radiomics System to Decode the Radiographic Phenotype
2017/10/31 by Joost J.M. van Griethuysen, Joost J. M. van Griethuysen, Andriy Fedorov +10 · 6,596 citations
Engineering · Medicine · #Advanced X-ray and CT Imaging #Biology #Computational biology #Computer science #Gene #Genetics #MRI in cancer diagnosis #Medicine #Phenotype #Radiography #Radiology #Radiomics #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.1158/0008-5472.can-17-0339
published in Cancer Research 77(21), e104-e107 (American Association for Cancer Research)
openalex publication_date 2017/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract
Abstract Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io. With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104–7. ©2017 AACR.
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- The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
- Emerging Applications of Artificial Intelligence in Neuro-Oncology
- Machine and deep learning methods for radiomics
- Deep learning‐based AI model for signet‐ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer
- PD-L1 Immuno-PET Reveals Systemic Effects of Localized Oncolytic Virotherapy in a Mouse Model of Head and Neck Cancer
- ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization
- The Multi-View Paradigm Shift in MRI Radiomics: Predicting MGMT Methylation in Glioblastoma
- Breast Cancer Neoadjuvant Chemotherapy Treatment Response Prediction Using Aligned Longitudinal MRI and Clinical Data
- Radiomics and Clinical Features in Predictive Modelling of Brain Metastases Recurrence
- Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-guided Subtyping and Lesion-Wise Model Ensemble
- Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques
- A Novel Patch-Based TDA Approach for Computed Tomography Imaging
- Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection
- LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysis
- Beyond Size and Growth: Rethinking Lung Cancer Screening with AI Based Nodule Detection and Diagnosis
- Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI
- UAM: A Unified Attention-Mamba Backbone of Multimodal Framework for Tumor Cell Classification
- PySERA: Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics
- Synergy vs. Noise: Performance-Guided Multimodal Fusion For Biochemical Recurrence-Free Survival in Prostate Cancer
- TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks
- AGENet: Adaptive Edge-aware Geodesic Distance Learning for Few-Shot Medical Image Segmentation
- Domain-Adaptive Transformer for Data-Efficient Glioma Segmentation in Sub-Saharan MRI
- A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
- HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT
- Multi-scale radiomics in pelvic MRI for endometriosis subtyping: highlighting data heterogeneity constraints
- Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer
- Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma
- Long-term prognostic value of CT-based high-risk coronary lesion attributes and radiomic features of pericoronary adipose tissue in diabetic patients
- Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning–based Pharmacokinetic Quantification of DCE MRI
- Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer
- Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography
- Pulmonary Artery Endothelial Cells from Patients with Pulmonary Arterial Hypertension Exhibit Heterogeneous Responses to Their Mechanical Microenvironment
- CBCT-Based Clinico-Radiomic Nomogram Predicting Preoperative Mandibular Third Molar Difficulty: Development/Validation
- MRI‐Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study
- A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
- Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter‐Observer Variability
- A multicenter study on radiomic features from T2-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics
- Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
- Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics
- Radiomics feature analysis for survival prediction in multiple myeloma: An automated PET/CT approach
- Machine Learning based Analysis for Radiomics Features Robustness in Real-World Deployment Scenarios
- Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
- Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis
- Spherical Radiomics -- A Novel Approach to Glioblastoma Radiogenomic Analysis of Heterogeneity
- REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
- Seeing More, Treating Smarter: Role of Long-Axial Field-of-View PET-CT in The Evolution of Theranostics
- PyRadiomics-cuda: a GPU-accelerated 3D features extraction from medical images within PyRadiomics
- Semi-Supervised Radiomics for Glioblastoma IDH Mutation: Limited Labels, Data Sensitivity, and SHAP Interpretation
- UltimateSynth: MRI Physics for Pan-Contrast AI
- Traumatic Brain Injury Segmentation using an Ensemble of Encoder-decoder Models
- Tumor Synthesis conditioned on Radiomics
- Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis
- Pericytes promote metastasis by regulating tumor local vascular tone and hemodynamics
- Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals
- TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
- Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features
- ConRad: Efficient Conformal Prediction for Radiomics
- AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT
- Cardiac magnetic resonance-derived extracellular volume radiomics in reperfused ST-elevation myocardial infarction: long-term prognostic value and risk stratification
- A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction
- A pilot study of magnetic resonance fingerprinting and radiomics analysis in autosomal dominant polycystic kidney disease
- Impact of partial volume correction on radiomics reproducibility in theranostic SPECT/CT imaging
- Graph-Radiomic Learning (GrRAiL) Descriptor to Characterize Imaging Heterogeneity in Confounding Tumor Pathologies
- Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
- Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson Disease
- Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imaging
- Live(r) Die: Predicting Survival in Colorectal Liver Metastasis
- Magnetic Resonance Imaging Virtual Liver Biopsy Using Radiomics Analysis for the Assessment of Chronic Liver Disease
- Leveraging Support Vector Regression, Radiomics and Dosiomics for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)
- AI-based response assessment and prediction in longitudinal imaging for brain metastases treated with stereotactic radiosurgery
- Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
- Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach
- Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks
- DARWIN: A Highly Flexible Platform for Imaging Research in Radiology
- Controllable Skin Synthesis via Lesion-Focused Vector Autoregression Model
- AT-CXR: Uncertainty-Aware Agentic Triage for Chest X-rays
- Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
- Explain and Monitor Deep Learning Models for Computer Vision using Obz AI
- Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging
- AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases
- AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
- Data-Driven Abdominal Phenotypes of Type 2 Diabetes in Lean, Overweight, and Obese Cohorts
- Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction
- A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data
- Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis
- Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
- Large Kernel MedNeXt for Breast Tumor Segmentation and Self-Normalizing Network for pCR Classification in Magnetic Resonance Images
- The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information
- Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease
- Joint Holistic and Lesion Controllable Mammogram Synthesis via Gated Conditional Diffusion Model
- 3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management
- MRI-based radiomic signature for MYCN amplification prediction of pediatric abdominal neuroblastoma
- A Novel Nomogram for Predicting Meningioma Grade Based on Radiomics Features and Clinical Characteristics
- Radiomic features of multi-parametric MRI present stable associations with analogous histological features in brain cancer patients
- Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
- Computer-Assisted Analysis of Biomedical Images
- Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning–based Risk Stratification System Using US Cine-Clip Images
- Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study
- RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features
- X-ray transferable polyrepresentation learning
- A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization
- Automated determination of hip arthrosis on the Kellgren–Lawrence scale in pelvic digital radiographs scans using machine learning
- Cardiovascular disease classification using radiomics and geometric features from cardiac CT
- Radiomic fingerprints for knee MR images assessment
- Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration
- Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
- Hyperbolic Kernel Graph Neural Networks for Neurocognitive Decline Analysis from Multimodal Brain Imaging
- Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction
- Lymph Node Graph Neural Networks for Cancer Metastasis Prediction
- Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics
- CT radiomic features reproducibility of virtual non-contrast series derived from photon-counting CCTA datasets using a novel calcium-preserving reconstruction algorithm compared with standard non-contrast series: focusing on epicardial adipose tissue
- Radiomics-based detection of acute myocardial infarction on noncontrast enhanced midventricular short-axis cine CMR images
- Radiomics features of the cardiac blood pool to indicate hemodynamic changes in pulmonary hypertension (PH) due to heart failure with preserved ejection fraction (PH-HFpEF)
- Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images
- Fully-Automated Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice ROI Parsing: A Physician-Inspired Approach
- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging
- Pixels to Prognosis: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures Across Multicentre NSCLC Data
- Structured Proxy Features for Multimodal NSCLC Survival Prediction from Pretreatment CT
- Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
- Using a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Features
- A new radiomics feature: image frequency analysis
- Lipid-related radiomics of low-echo carotid plaques is associated with diabetic stroke and non-diabetic coronary heart disease
- Early-life gut inflammation drives sex-dependent shifts in the microbiome-endocrine-brain axis
- Artificial Intelligence–Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases
- Multimodal domain adaptation under label shift and blockwise missing modalities
- Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients
- ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
- Probabilistic approach to longitudinal response prediction: application to radiomics from brain cancer imaging
- Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
- Class-Aware Adversarial Transformers for Medical Image Segmentation
- Imaging Exploration of Molecular Subtypes in Tongue Squamous Cell Carcinoma
- HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models
- VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing
- Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma
- Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas
- Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
- Deep Anatomical Federated Network (Dafne): An Open Client-Server Framework for Continuous, Collaborative Improvement of Deep Learning–based Medical Image Segmentation
- Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features
- Radiomic features on multiparametric MRI for differentiating pseudoprogression from recurrence in high-grade gliomas
- Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma
- Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. [europepmc]
- Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. [europepmc]
- Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. [europepmc]
- Emerging Applications of Artificial Intelligence in Neuro-Oncology. [europepmc]
- Gray-level discretization impacts reproducible MRI radiomics texture features. [europepmc]
- Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. [europepmc]
- A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data. [europepmc]
- Repeatability of Multiparametric Prostate MRI Radiomics Features. [europepmc]
- Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives. [europepmc]
- An image-based deep learning framework for individualizing radiotherapy dose. [europepmc]
- A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. [europepmc]
- Radiomics with artificial intelligence: a practical guide for beginners. [europepmc]
- Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study. [europepmc]
- Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region. [europepmc]
- How to develop a meaningful radiomic signature for clinical use in oncologic patients. [europepmc]
- Machine and deep learning methods for radiomics. [europepmc]
- Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. [europepmc]
- Radiomics in medical imaging-"how-to" guide and critical reflection. [europepmc]
- FeAture Explorer (FAE): A tool for developing and comparing radiomics models. [europepmc]
- Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. [europepmc]
- Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma. [europepmc]
- Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. [europepmc]
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology. [europepmc]
- Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography. [europepmc]
- Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. [europepmc]
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