Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging

Mackenzie, Alistair; Thomson, Emma L.; Mitchell, Melissa; Elangovan, Premkumar; Ongeval, Chantal Van; Cockmartin, Lesley; Warren, Lucy M.; Wilkinson, Louise; Wallis, Matthew; Given-Wilson, Rosalind; Dance, David R.; Young, Kenneth C.

doi:10.1007/s00330-021-08197-x

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…The weight of the polynomials is concentrated within a Gaussian envelope with spread

{\sigma _u}

, where

a_u^2 = \ 2\pi {\sigma _u}^2

. In this study, we used

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values of 15, 20, and 21, for each image reconstructed ROI sizes of 11 mm, 2 14 mm, 2 and 15 mm 2 (10‐bit depth). These parameters were selected based on values previously reported on the literature 14 …”

Section: Methodsmentioning

confidence: 99%

“…Virtual clinical trials (VCTs) have been widely used as a rapid and cost‐effective alternative for evaluating and optimizing imaging technologies 1–5 . VCTs allow for the simulation of images with voxel‐wise ground‐truth, which is not available in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

“…Virtual clinical trials (VCTs) have been widely used as a rapid and cost-effective alternative for evaluating and optimizing imaging technologies. [1][2][3][4][5] VCTs allow for the simulation of images with voxel-wise ground-truth, which is not available in clinical practice.The images and ground truth can be used to evaluate the performance of imaging systems, modalities, and techniques. 1,6,7 The ground truth yields information regarding the strengths and weaknesses of imaging systems,providing valuable knowledge that can be used for myriad purposes including to support the regulatory approval of novel imaging systems.…”

Section: Introductionmentioning

confidence: 99%

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Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

et al. 2022

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Purpose Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings. Methods Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I–IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II–IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores. Results The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue. Conclusions For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs.

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“…The weight of the polynomials is concentrated within a Gaussian envelope with spread

{\sigma _u}

, where

a_u^2 = \ 2\pi {\sigma _u}^2

. In this study, we used

{a_u}

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

et al. 2022

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“…12,13 These types of study have their own individual strengths and weaknesses but they have in common the ability to test the effect of different imaging parameters, breast and cancer types without additional risk to women, and have the potential to investigate the effectiveness of imaging techniques, such as 2D imaging, DBT, and DBT with synthetic 2D. 10,14 In this study, we have used a four-alternative forced choice (AFC) experiment to estimate the threshold diameters of masses and calcifications to investigate the effectiveness of imaging techniques, such as 2D imaging, DBT, and DBT with synthetic 2D. There are many studies using AFC to examine the effect of different imaging technologies, but there are challenges to compare results as they depend on the system being tested, the phantom type, 12 the detector type, choice of threshold, material or attenuation coefficient of lesions, the tomosynthesis acquisition method, and the reconstruction and processing software.…”

Section: Introductionmentioning

confidence: 99%

“…VCTs have been undertaken using a number of methods by adapting clinical images, 11 imaging physical phantoms, or entirely simulating breasts 12,13 . These types of study have their own individual strengths and weaknesses but they have in common the ability to test the effect of different imaging parameters, breast and cancer types without additional risk to women, and have the potential to investigate the effectiveness of imaging techniques, such as 2D imaging, DBT, and DBT with synthetic 2D 10,14 …”

Section: Introductionmentioning

confidence: 99%

Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial

et al. 2021

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The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers. Methods: Mathematical voxel breast phantoms with glandularities by volume of 9%,18%,and 30% with a thickness of 53 mm were created.Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29 kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists. Results: The threshold diameters of the lesions were 6.2, 4.9, and 6.7 mm (masses) and 225, 370, and 399 µm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of illdefined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p = 0.059) and 18% (p = 0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p < 0.001) for all glandularities.

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Multi‐scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model‐based perceptual similarity

Jiang

Zhou

et al. 2022

Medical Physics

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Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. Method: To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The meansquared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. Results: 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the Gongfa Jiang, Zilong He, and Yuanpin Zhou contributed equally to this work.

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Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging

Cited by 12 publications

References 35 publications

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial

Multi‐scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model‐based perceptual similarity

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