ArticleJAMA network open2018
Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial.
Article in JAMA network open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers.
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Who cites it
127 citing papers in PubMed.
- Synthetic control methods for n-of-1 and parallel-group trials in Alzheimer's disease: A proof-of-concept study using the I-CONECT.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Radiation dose comparison of photon-counting breast CT and mammography: A phantom study.European journal of radiology open · 2026Article
- A Simple, Dynamic Geometric Phantom for MRI and CT Reconstruction Pipelines: Beyond Shepp-Logan.Magnetic resonance in medicine · 2026Article
- A virtual imaging framework for three-dimensional quantitative optoacoustic tomography using stochastic numerical breast phantoms.Photoacoustics · 2026Article
- Mammo-GAN-Assisted Deep Network Training Scheme for Lesion Detection.Journal of imaging informatics in medicine · 2026Article
- Synthetic-to-Clinical Ensemble Learning for Volumetric Breast Tumor Segmentation in Digital Breast Tomosynthesis Under Limited Annotated Data.Diagnostics (Basel, Switzerland) · 2026Article
- Accurate estimation of intravoxel incoherent motion parameters based on implicit neural representation.Medical physics · 2026Article
- Ensuring the quality of in silico evidence: application to medical devices.Briefings in bioinformatics · 2026Article
- In Silico Digital Breast Tomosynthesis Dataset for the Comparative Analysis of Deep Learning Models in Tumor Segmentation.Journal of imaging informatics in medicine · 2026Article
- Demographic distribution matching between real-world and virtual phantom population.Medical physics · 2026Article
- Predicting Region of Interest in Human Visual Search Based on Statistical Texture and Gabor Features.ArXiv · 2026Article
- Utility of the virtual imaging trials methodology for objective characterization of AI systems and training data.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2025Article
- Consistent performance between medical experts and non-expert readers in forced-choice lesion-detection tasks with PET images.Medical physics · 2025Article
- Hybrid simulation of breast CT for assessing microcalcification detectability.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- An end-to-end CT simulation framework with graphical user interface and sample scanner models.Medical physics · 2025Article
- Privacy-by-Design Approach to Generate Two Virtual Clinical Trials for Multiple Sclerosis and Release Them as Open Datasets: Evaluation Study.Journal of medical Internet research · 2025Article
- Convolutional neural network model observers discount signal-like anatomical structures during search in virtual digital breast tomosynthesis phantoms.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Predicting the quality of digital mammography from the perspective of detectability of microcalcifications: A Radiomics approach.Medical physics · 2025Article
67 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Expensive and lengthy clinical trials can delay regulatory evaluation of innovative technologies, affecting patient access to high-quality medical products. Simulation is increasingly being used in product development but rarely in regulatory applications. Objectives: To conduct a computer-simulated imaging trial evaluating digital breast tomosynthesis (DBT) as a replacement for digital mammography (DM) and to compare the results with a comparative clinical trial. Design, Setting, and Participants: The simulated Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) trial was designed to replicate a clinical trial that used human patients and radiologists. Images obtained with in silico versions of DM and DBT systems via fast Monte Carlo x-ray transport were interpreted by a computational reader detecting the presence of lesions. A total of 2986 synthetic image-based virtual patients with breast sizes and radiographic densities representative of a screening population and compressed thicknesses from 3.5 to 6 cm were generated using an analytic approach in which anatomical structures are randomly created within a predefined breast volume and compressed in the craniocaudal orientation. A positive cohort contained a digitally inserted microcalcification cluster or spiculated mass. Main Outcomes and Measures: The trial end point was the difference in area under the receiver operating characteristic curve between modalities for lesion detection. The trial was sized for an SE of 0.01 in the change in area under the curve (AUC), half the uncertainty in the comparative clinical trial. Results: In this trial, computational readers analyzed 31 055 DM and 27 960 DBT cases from 2986 virtual patients with the following Breast Imaging Reporting and Data System densities: 286 (9.6%) extremely dense, 1200 (40.2%) heterogeneously dense, 1200 (40.2%) scattered fibroglandular densities, and 300 (10.0%) almost entirely fat. The mean (SE) change in AUC was 0.0587 (0.0062) (P < .001) in favor of DBT. The change in AUC was larger for masses (mean [SE], 0.0903 [0.008]) than for calcifications (mean [SE], 0.0268 [0.004]), which was consistent with the findings of the comparative trial (mean [SE], 0.065 [0.017] for masses and -0.047 [0.032] for calcifications). Conclusions and Relevance: The results of the simulated VICTRE trial are consistent with the performance seen in the comparative trial. While further research is needed to assess the generalizability of these findings, in silico imaging trials represent a viable source of regulatory evidence for imaging devices.
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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.