Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial

Badano, Aldo; Graff, Christian; Badal, Andreu; Sharma, Diksha; Zeng, Rongping; Samuelson, F. W.; Glick, Stephen J.; Myers, Kyle J.

doi:10.1001/jamanetworkopen.2018.5474

Cited by 155 publications

(216 citation statements)

References 41 publications

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“…The endpoint of this in silico trial was the difference in AUC curves between the two modalities across all the breast density categories. The results of this trial were consistent with the performance reported in the comparative trial …”

Section: Resultssupporting

confidence: 88%

“…The final output of the readers’ performances was the area under the ROC curve (AUC). Further details are available in the VICTRE manuscript …”

Section: Methodsmentioning

confidence: 99%

“…Often, performing a trial is expensive and can delay regulatory evaluation and hinder early patient access to novel imaging devices. In order to demonstrate an alternative approach, a recent effort known as the VICTRE project, an acronym for Virtual Imaging Clinical Trial for Regulatory Evaluation, demonstrated the replication of one such clinical trial using only in silico tools and compared results in terms of imaging performance between the prior human trial and the computational replica. The VICTRE imaging trial consisted of generating and imaging 2986 digital breast models using in silico models of digital mammography (DM) and digital breast tomosynthesis (DBT) systems.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Resultssupporting

confidence: 88%

“…The final output of the readers’ performances was the area under the ROC curve (AUC). Further details are available in the VICTRE manuscript …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Monte Carlo (MC) methods provide a gold‐standard approach to simulating noisy x‐ray projections. Recent works showed that a MC‐based method, as well as the single iteration x‐ray tracing used here, are capable of reproducing the outcome of real‐life DBT clinical trial using exclusively artificial data. However, even though both methods perform with an acceptable degree of realism, the computational time required by MC‐based methods is up to 65 times higher than required by the algorithm adopted here .…”

Section: Resultsmentioning

confidence: 99%

“…Recent works showed that a MC‐based method, as well as the single iteration x‐ray tracing used here, are capable of reproducing the outcome of real‐life DBT clinical trial using exclusively artificial data. However, even though both methods perform with an acceptable degree of realism, the computational time required by MC‐based methods is up to 65 times higher than required by the algorithm adopted here . As VCTs may involve large numbers of simulations (potentially

10^{4} - 10^{5}

simulated images), our goal is to develop a rapid alternative with sufficient realism to obtain accurate results.…”

Section: Resultsmentioning

confidence: 99%

Technical Note: Noise models for virtual clinical trials of digital breast tomosynthesis

Borges¹,

Barufaldi

Caron

et al. 2019

Medical Physics

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Purpose To investigate the use of an affine‐variance noise model, with correlated quantum noise and spatially dependent quantum gain, for the simulation of noise in virtual clinical trials (VCT) of digital breast tomosynthesis (DBT). Methods Two distinct technologies were considered: an amorphous‐selenium (a‐Se) detector with direct conversion and a thallium‐doped cesium iodide (CsI(Tl)) detector with indirect conversion. A VCT framework was used to generate noise‐free projections of a uniform three‐dimensional simulated phantom, whose geometry and absorption match those of a polymethyl methacrylate (PMMA) uniform physical phantom. The noise model was then used to generate noisy observations from the simulated noise‐free data, while two clinically available DBT units were used to acquire projections of the PMMA physical phantom. Real and simulated projections were then compared using the signal‐to‐noise ratio (SNR) and normalized noise power spectrum (NNPS). Results Simulated images reported errors smaller than 4.4% and 7.0% in terms of SNR and NNPS, respectively. These errors are within the expected variation between two clinical units of the same model. The errors increase to 65.8% if uncorrelated models are adopted for the simulation of systems featuring indirect detection. The assumption of spatially independent quantum gain generates errors of 11.2%. Conclusions The investigated noise model can be used to accurately reproduce the noise found in clinical DBT. The assumption of uncorrelated noise may be adopted if the system features a direct detector with minimal pixel crosstalk.

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Artificial intelligence in breast imaging: Current situation and clinical challenges

et al. 2023

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Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer‐related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

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Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial

Cited by 155 publications

References 41 publications

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

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

Technical Note: Noise models for virtual clinical trials of digital breast tomosynthesis

Artificial intelligence in breast imaging: Current situation and clinical challenges

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