Literature DB >> 31228352

Technical Note: In silico imaging tools from the VICTRE clinical trial.

Diksha Sharma1, Christian G Graff1, Andreu Badal1, Rongping Zeng1, Purva Sawant1, Aunnasha Sengupta1, Eshan Dahal1, Aldo Badano1.   

Abstract

PURPOSE: In silico imaging clinical trials are emerging alternative sources of evidence for regulatory evaluation and are typically cheaper and faster than human trials. In this Note, we describe the set of in silico imaging software tools used in the VICTRE (Virtual Clinical Trial for Regulatory Evaluation) which replicated a traditional trial using a computational pipeline.
MATERIALS AND METHODS: We describe a complete imaging clinical trial software package for comparing two breast imaging modalities (digital mammography and digital breast tomosynthesis). First, digital breast models were developed based on procedural generation techniques for normal anatomy. Second, lesions were inserted in a subset of breast models. The breasts were imaged using GPU-accelerated Monte Carlo transport methods and read using image interpretation models for the presence of lesions. All in silico components were assembled into a computational pipeline. The VICTRE images were made available in DICOM format for ease of use and visualization.
RESULTS: We describe an open-source collection of in silico tools for running imaging clinical trials. All tools and source codes have been made freely available.
CONCLUSION: The open-source tools distributed as part of the VICTRE project facilitate the design and execution of other in silico imaging clinical trials. The entire pipeline can be run as a complete imaging chain, modified to match needs of other trial designs, or used as independent components to build additional pipelines. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Entities:  

Keywords:  Monte Carlo methods; clinical trials; digital breast tomosynthesis; digital mammography; in silico models

Year:  2019        PMID: 31228352     DOI: 10.1002/mp.13674

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  4 in total

1.  Computational reader design and statistical performance evaluation of an in-silico imaging clinical trial comparing digital breast tomosynthesis with full-field digital mammography.

Authors:  Rongping Zeng; Frank W Samuelson; Diksha Sharma; Andreu Badal; Graff G Christian; Stephen J Glick; Kyle J Myers; Aldo Badano
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-26

2.  Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening.

Authors:  Hanna Tomic; Anna Bjerkén; Gustav Hellgren; Kristin Johnson; Daniel Förnvik; Sophia Zackrisson; Anders Tingberg; Magnus Dustler; Predrag R Bakic
Journal:  J Med Imaging (Bellingham)       Date:  2022-06-06

3.  In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable.

Authors:  Aldo Badano
Journal:  Trials       Date:  2021-01-19       Impact factor: 2.279

4.  Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.

Authors:  Giuseppe Di Sciacca; Giulia Maffeis; Andrea Farina; Alberto Dalla Mora; Antonio Pifferi; Paola Taroni; Simon Arridge
Journal:  J Biomed Opt       Date:  2022-03       Impact factor: 3.758

  4 in total

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