Special Issue Information

Dear Colleagues,

Breast imaging is one of the most exciting subspecialties in radiology. The need for personalized medicine has changed our vision of the field, giving even more importance to multimodality and to the integration of both Artificial Intelligence and human resources. Consequently, evaluating the application of radiomics could be of particular interest. In addition, examining the real impact of the most recent advancements of standard imaging techniques, such as digital breast tomosynthesis (DBT) and automated breast ultrasound (ABUS), on breast cancer prevention could be incredibly challenging.

Contrast-enhanced spectral mammography (CESM) could be considered a true alternative to the most expensive magnetic resonance imaging (MRI). On the other side, the evaluation of fast MRI could have a great impact on breast cancer prevention, especially in young women with dense breast.

This Special Issue aims to present and discuss the most recent advancements in breast imaging. Original research articles and reviews are welcome. Research areas may include (but are not limited to) all the aforementioned topics. Articles on breast cancer screening programs are welcome, especially those dealing with DBT and ABUS. Finally, articles on breast interventional radiology, with particular regard to vacuum-assisted biopsy or excision system, are also welcome.

I look forward to receiving your contributions.

Dr. Daniele Tari
Guest Editor

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Keywords

breast imaging
breast cancer screening
digital breast tomosynthesis (DBT)
3D automated breast ultrasound (ABUS)
contrast-enhanced spectral mammography (CESM)
fast-MRI
vacuum-assisted breast biopsy (VABB)
breast lesion excision system (BLES)
radiomics
computer-aided detection (CAD)

Published Papers (3 papers)

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Open AccessEditorial

Special Issue “New Advances in Breast Imaging”

Daniele Ugo Tari

Tomography 2022, 8(4), 1702-1703; https://doi.org/10.3390/tomography8040142 - 28 Jun 2022

Viewed by 1081

Abstract

Breast cancer (BC) is the most common cancer in women of all ages, with more than 2 million diagnoses every year and a high economic and psychological impact on both the health care system and the population [...] Full article

(This article belongs to the Special Issue New Advances in Breast Imaging)

Research

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Open AccessArticle

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network

Timothy J. Allen

Leah C. Henze Bancroft

and

Tomography 2023, 9(3), 967-980; https://doi.org/10.3390/tomography9030079 - 10 May 2023

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Abstract

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was −2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model. Full article

(This article belongs to the Special Issue New Advances in Breast Imaging)

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Open AccessArticle

Supine versus Prone 3D Abus Accuracy in Breast Tumor Size Evaluation

Charlotte Marguerite Lucille Trombadori

and

Tomography 2022, 8(4), 1997-2009; https://doi.org/10.3390/tomography8040167 - 12 Aug 2022

Viewed by 1171

Abstract

Breast-conserving surgery (BCS) with negative resection margins decreases the locoregional recurrence rate. Breast cancer size is one of the main determinants of Tumor-Node-Metastasis (TNM) staging. Our study aimed to investigate the accuracy of supine 3D automated breast ultrasound (3D ABUS) compared to prone 3D ABUS in the evaluation of tumor size in breast cancer patient candidates for BCS. In this prospective two-center study (Groups 1 and 2), we enrolled patients with percutaneous biopsy-proven early-stage breast cancer, in the period between June 2019 and May 2020. Patients underwent hand-held ultrasound (HHUS), contrast-enhanced magnetic resonance imaging (CE-MRI) and 3D ABUS—supine 3D ABUS in Group 1 and prone 3D ABUS in Group 2. Histopathological examination (HE) was considered the reference standard. Bland–Altman analysis and plots were used. Eighty-eight patients were enrolled. Compared to prone, supine 3D ABUS showed better agreement with HE, with a slight tendency toward underestimation (mean difference of −2 mm). Supine 3D ABUS appears to be a useful tool and more accurate than HHUS in the staging of breast cancer. Full article

(This article belongs to the Special Issue New Advances in Breast Imaging)

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