ArticleTomography (Ann Arbor, Mich.)2019
ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.
Article in Tomography (Ann Arbor, Mich.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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
23 citing papers in PubMed.
- Evidence of a cognitive bias in the quantification of COVID-19 with CT: an artificial intelligence randomised clinical trial.Scientific reports · 2023Trial
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications.Life (Basel, Switzerland) · 2025Article
- Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project.Journal of imaging informatics in medicine · 2024Article
- Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.Tomography (Ann Arbor, Mich.) · 2023Article
- An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: a Methodological Approach.Journal of digital imaging · 2023Article
- BlueLight: An Open Source DICOM Viewer Using Low-Cost Computation Algorithm Implemented with JavaScript Using Advanced Medical Imaging Visualization.Journal of digital imaging · 2023Article
- QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research.European radiology experimental · 2023Article
- Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials.Tomography (Ann Arbor, Mich.) · 2023Review
- Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Risk Stratification System Using US Cine-Clip Images.Radiology. Artificial intelligence · 2022Article
- Artificial intelligence and machine learning in cancer imaging.Communications medicine · 2022Review
- Towards a better understanding of annotation tools for medical imaging: a survey.Multimedia tools and applications · 2022Article
- Deep Radiotranscriptomics of Non-Small Cell Lung Carcinoma for Assessing Molecular and Histology Subtypes with a Data-Driven Analysis.Diagnostics (Basel, Switzerland) · 2021Article
- Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.International journal of molecular sciences · 2021Article
- Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.Radiology. Artificial intelligence · 2021Article
- Article
- DICOM re-encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules.Medical physics · 2020Article
- Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Quantitative Imaging Enters the Clinical Arena: A Personal Viewpoint.Tomography (Ann Arbor, Mich.) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.
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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.