The threshold detectable mass diameter for 2D-mammography and digital breast tomosynthesis

Hadjipanteli, Andria; Elangovan, Premkumar; Mackenzie, Alistair; Wells, Kevin; Dance, David R.; Young, Kenneth C.

doi:10.1016/j.ejmp.2018.11.014

Cited by 22 publications

(20 citation statements)

References 43 publications

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“…These concerns lead to some simulation studies using realistic breast tissue and real observers aiming to explain the above findings. It was concluded that DBT can outperform DM in masses detectability 20,21. However, the minimum detectable calcification diameter of DM and DBT were found to be 164±5 μm 210±5 μm respectively at standard dose 22.…”

Section: Results: Studies On the Optimal Use Of Digital Breast Tomosymentioning

confidence: 99%

<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

Hadjipanteli

Kontos

Constantinidou

2019

CMAR

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Aim: Digital Breast Tomosynthesis (DBT), with or without Digital Mammography (DM) or Synthetic Mammography (SM), has been introduced or is under consideration for its introduction in breast cancer screening in several countries, as it has been shown that it has advantages over DM. Despite this there is no agreement on how to implement DBT in screening, and in many cases there is a lack of official guidance on the optimum usage of each commercially available system. The aim of this review is to carry out a manufacturerspecific summary of studies on the implementation of DBT in breast cancer screening. Methods: An exhaustive literature review was undertaken to identify clinical observer studies that evaluated at least one of five common metrics: sensitivity, specificity, area under the curve (AUC) of the receiver-operating characteristics (ROC) analysis, recall rate and cancer detection rate. Four common DBT implementation methods were discussed in this review: (1) DBT, (2) DM with DBT, (3) 1-view DBT with or without 1-view DM or 2-view DM and (4) DBT with SM. Results: A summary of 89 studies, selected from a database of 677 studies, on the assessment of the implementation of DBT in breast cancer screening is presented in tables and discussed in a manufacturer-and metric-specific approach. Much more studies were carried out using some DBT systems than others. For one implementation method of DBT by one manufacturer there is a shortage of studies, for another implementation there are conflicting results. In some cases, there is a strong agreement between studies, making the advantages and disadvantages of each system clear. Conclusion: The optimum implementation method of DBT in breast screening, in terms of diagnostic benefit and patient radiation dose, for one manufacturer does not necessarily apply to other manufacturers.

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Section: Results: Studies On the Optimal Use Of Digital Breast Tomosymentioning

confidence: 99%

<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

Hadjipanteli

Kontos

Constantinidou

2019

CMAR

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“…30,213,[278][279][280][281][282][283][284][285][286][287][288][289] Recent VCTs have attempted to predict the ranking and the magnitude of improvement of breast imaging technologies as seen by human observers. 26,278,[290][291][292] In one example, VCTs were used in the Optimam project to evaluate the smallest detectable diameter of various lesions, showing that digital breast tomosynthesis (DBT) is superior to digital mammography (DM) for masses, 290 while the converse is true for calcifications. 291 Subsequent work 292 confirmed a significant difference between DM and DBT in mass detection but showed no significant difference between narrow and wide angle DBT although a trend toward superior performance for wide angle DBT was noted.…”

Section: Breast Imagingmentioning

confidence: 99%

“…The choice of evaluatory metrics is critical in designing and validating VCTs. For example, the Optimam results [290][291][292] were reported in terms of minimum detectable diameter. Although these VCTs accurately predicted rankings of the imaging technologies that were concordant with clinical data, precise validation was not possible since ground truth is lacking for the minimum detectable diameter of lesions in clinical cases (such data do exist for phantoms).…”

Section: Verification Validations and Inferencementioning

confidence: 99%

Virtual clinical trials in medical imaging: a review

et al. 2020

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The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

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“…With the aim of improving its diagnostic performance and hence also helping to reduce the rate of recalls, in the last decades research efforts have been directed towards new pseudo-3D or 3D X-ray breast imaging technologies such as digital breast tomosynthesis (DBT) [3][4][5] and breast computed tomography [6], respectively. These techniques allow to overcomein part or totally, respectivelythe overlap of normal and pathological tissues in the direction of the incident beam, which can decrease the visibility of malignant abnormalities or simulate the appearance of a lesion [7][8][9]. DBT acquires many two-dimensional projections from various angular positions of the X-ray tube around the compressed breast, at a comparable level of radiation dose with respect to DM [5].…”

Section: Introductionmentioning

confidence: 99%

A deep learning classifier for digital breast tomosynthesis

et al. 2021

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To develop a computerized detection system for the automatic classification of the presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, based on a deep convolutional neural network (DCNN). Materials and Methods: Three DCNN architectures working at image-level (DBT slice) were compared: two stateof-the-art pre-trained DCNN architectures (AlexNet and VGG19) customized through transfer learning, and one developed from scratch (DBT-DCNN). To evaluate these DCNN-based architectures we analysed their classification performance on two different datasets provided by two hospital radiology departments. DBT slice images were processed following normalization, background correction and data augmentation procedures. The accuracy, sensitivity, and area-under-the-curve (AUC) values were evaluated on both datasets, using receiver operating characteristic curves. A Grad-CAM technique was also implemented providing an indication of the lesion position in the DBT slice. Results: Accuracy, sensitivity and AUC for the investigated DCNN are in-line with the best performance reported in the field. The DBT-DCNN network developed in this work showed an accuracy and a sensitivity of (90% ± 4%) and (96% ± 3%), respectively, with an AUC as good as 0.89 ± 0.04. A k-fold cross validation test (with k = 4) showed an accuracy of 94.0% ± 0.2%, and a F1-score test provided a value as good as 0.93 ± 0.03. Grad-CAM maps show high activation in correspondence of pixels within the tumour regions. Conclusions: We developed a deep learning-based framework (DBT-DCNN) to classify DBT images from clinical exams. We investigated also a possible application of the Grad-CAM technique to identify the lesion position.

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The threshold detectable mass diameter for 2D-mammography and digital breast tomosynthesis

Cited by 22 publications

References 43 publications

<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

Virtual clinical trials in medical imaging: a review

A deep learning classifier for digital breast tomosynthesis

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