The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

2020/03/10 by Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah +75 · 25 citations
Computer Science · Medicine · #AI in cancer detection #Colorectal Cancer Surgical Treatments #Radiomics and Machine Learning in Medical Imaging

paper · doi:10.1148/radiol.2020191145

openalex publication_date 2020/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

See also the editorial by Kuhl and Truhn in this issue.

Citations

• Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework
• Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
• Textural features corresponding to textural properties
• Textural Features for Image Classification
• NOTES ON CONTINUOUS STOCHASTIC PHENOMENA
• Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement
• Least squares quantization in PCM
• Computational Radiomics System to Decode the Radiographic Phenotype

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• Towards Integrated Clinical-Computational Nuclear Medicine
• PySERA: Open-Source Standardized Python Library for Automated, Scalable, and Reproducible Handcrafted and Deep Radiomics
• Radiomics in clinical radiology: advances, challenges, and future directions
• Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI
• Longitudinal DCE MRI Vascular Textures: Radiologic and Biologic Insights for pCR Prediction in HER2-Negative Breast Cancer
• Identifying the optimal time point for adaptive re-planning in prostate cancer radiotherapy to minimise rectal toxicity using normal tissue imaging biomarkers
• Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer
• Potentials and caveats of AI in hybrid imaging
• CBCT-Based Clinico-Radiomic Nomogram Predicting Preoperative Mandibular Third Molar Difficulty: Development/Validation
• <scp>Reproducible Radiomics Features from Multi‐MRI‐Scanner Test–Retest‐Study: Influence on Performance and Generalizability of Models</scp>
• A multicenter study on radiomic features from T₂-weighted images of a\n customized MR pelvic phantom setting the basis for robust radiomic models in\n clinics
• Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients
• Imaging the Breast Cancer Microenvironment: Toward Interpretable MRI Biomarkers for Treatment Response
• WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data
• Tumor Synthesis conditioned on Radiomics
• AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT
• Impact of partial volume correction on radiomics reproducibility in theranostic SPECT/CT imaging
• Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers
• Random forest-based out-of-distribution detection for robust lung cancer segmentation
• AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
• Review: Adaptive Radiation Therapy for Head and Neck Cancer

Related

• Benchmarking features from different radiomics toolkits / toolboxes using Image Biomarkers Standardization Initiative
• Radiomics: Images Are More than Pictures, They Are Data
• Standardised convolutional filtering for radiomics
• A Global View of Standards for Open Image Data Formats and Repositories
• Radiomics: from qualitative to quantitative imaging