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

Tomic, Hanna; Bjerkén, Anna; Hellgren, Gustav; Johnson, Kristin; Förnvik, Daniel; Zackrisson, Sophia; Tingberg, Anders; Dustler, Magnus; Bakić, Predrag R.

doi:10.1117/1.jmi.9.3.033503

Cited by 4 publications

(2 citation statements)

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“…10,11 More general lesions can be used in multiple anatomical locations with minor modifications. Computational lesion models intended for simulated medical imaging 3,4,[12][13][14][15] use data from clinically imaged lesions as the basis for tuning model parameters; however, this immediately suggests an inconsistency with the representation of image-based computational lesions compared to images of real lesions. Lesions differing in high-frequency content-differences between spiculated margins, for example-can be rendered null by the imaging process.…”

Section: Introductionmentioning

confidence: 99%

Development and CT image‐domain validation of a computational lung lesion model for use in virtual imaging trials

et al. 2023

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Computational abnormalities (e.g., lesion models) for use in medical imaging simulation studies are frequently generated using data collected from clinical images. Although this approach allows for highly-customizable lesion detectability studies on clinical computed tomography (CT) data, the groundtruth lesion models produced with this method do not provide a sufficiently realistic lesion morphology for use with current anthropomorphic simulation studies. This work is intended to demonstrate that the new anatomicallyinformed lesion model presented here is not inferior to the previous lesion model under CT imaging, and can therefore provide a more biologically-informed model for use with simulated CT imaging studies. Methods: The lesion model was simulated initially from a seed cell with 10 µm diameter placed in an anatomical location within segmented lung CT and was allowed to reproduce locally within the available solid angle in discrete timeintervals (corresponding to synchronous cell cycles) up to a size of ∼200 µm in diameter. Daughter cells of generation G were allowed also to reproduce on the next available time-step given sufficient space. At lesion sizes beyond 200 µm in diameter, the health of subregions of cells were tracked with a Markov chain technique, indicating which regions were likely to continue growing, which were likely stable, and which were likely to develop necrosis given their proximity to anatomical features and other lesion cells. For lesion sizes beyond 500 µm, the lesion was represented with three nested, triangulated surfaces (corresponding to proliferating, dormant, and necrotic regions), indicating how discrete volumes of the lesion were behaving at a particular time. Lesions were then assigned smoothly-varying material properties based on their cellular level health in each region, resulting in a multi-material lesion model. The lesions produced with this model were then voxelized and placed into lung CT images for comparison with both prior work and clinical data. This model was subject to an observer study in which cardiothoracic imaging radiologists assessed the realism of both clinical and synthetic lesions in CT images. Results:The useable outputs of this work were voxel-or surface-based, validated, computational lesions, at a scale clearly visible on clinical CT (3-4 cm). Analysis of the observer study results indicated that the computationallygenerated lesions were indistinguishable from clinical lesions (AUC = 0.49, 95% CI = [0.36, 0.61]) and non-inferior to an earlier image-based lesion modelindicating the advantage of the model for use in both hybrid CT images and in simulated CT imaging of the lungs.

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Section: Introductionmentioning

confidence: 99%

Development and CT image‐domain validation of a computational lung lesion model for use in virtual imaging trials

et al. 2023

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“…Dustler et al reported 36% reduction in false positives, when MI was performed together with DM, in two separate acquisitions 6 . A multimodality approach of simultaneous DBT and mechanical imaging (MI) -DBTMI, developed at our institution [8][9][10] , has potential to increase both sensitivity and specificity, without extending the clinical workflow.…”

Section: Introductionmentioning

confidence: 99%

Dose evaluation of simultaneous breast radiography and mechanical imaging

Bjerkén

Tomic

Bernhardsson

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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This study investigates the impact in terms of radiation dose when performing simultaneous digital breast tomosynthesis (DBT) and mechanical imaging (MI) -DBTMI. DBTMI has demonstrated the potential to increase specificity of cancer detection, and reduce unnecessary biopsies, as compared to digital mammography (DM) screening. The presence of the MI sensor during simultaneous image acquisition may increase the radiation dose when automatic exposure control is used. In this project, a radiation dose study was conducted on clinically available breast imaging systems with and without the MI sensor. We have investigated three approaches to analyse the dose increase in DBTMI, using (i) the estimates of average glandular dose (AGD) reported in DICOM headers of radiography images; (ii) AGD measured by a conventional dosemeter; and (iii) AGD measured by optically stimulated luminescence using NaCl pellets. The relative increase in AGD estimated from DICOM headers when using the MI sensor was on average 10.7% and 12.4%, for DM and DBT measurements, respectively. The relative increase in AGD using the conventional dosemeter was 11.2% in DM mode and 12.2% in DBT mode. The relative increase in AGD using NaCl pellets was 14.6% in DM mode. Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by 13% on average. The increase in dose is still below the acceptable values in mammography screening recommended by the European Guidelines.

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