Special Issue "Artificial Intelligence in Breast Cancer Screening"

A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Cancer Imaging".

Deadline for manuscript submissions: 31 March 2023 | Viewed by 4342

Special Issue Editors

Dr. Aimilia Gastounioti
E-Mail Website
Guest Editor
Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
Interests: computational imaging phenotypes; artificial intelligence; radiomics; deep learning; breast cancer risk
Dr. Debbie Bennett
E-Mail Website
Guest Editor
Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
Interests: breast cancer screening and diagnosis; mammography; breast ultrasound; breast intervention; clinical trials for breast cancer

Special Issue Information

Dear Colleagues,

Most developed healthcare systems have implemented breast cancer screening programs, initially using analog screen-film-based mammography systems and, over the last 20 years, transitioning to the use of fully digital systems (digital mammography and digital breast tomosynthesis). Much of the effort to improve breast cancer screening outcomes has focused on intensifying screening, e.g., double-reading instead of single-reading and more frequent or supplemental screening (with breast ultrasound or MRI), which entail increased resources and often come at a cost of higher false-positive rates. Furthermore, personalized breast cancer screening regimens tailored to an individual’s breast cancer risk are increasingly being advocated. The artificial intelligence (AI) revolution in computational imaging, driven by radiomic machine learning and more recently by deep learning, has also pervaded this complex landscape of breast cancer screening, including AI models for breast density evaluation, breast cancer risk assessment, breast cancer detection and prognosis, as well as enhancing efficiency in breast cancer care.

Therefore, it is with pleasure that we invite investigators to contribute to this Special Issue with original research articles, review articles, and meta-analysis articles addressing these topics, with special regard to their clinical and radiological implications for breast cancer screening.

Dr. Aimilia Gastounioti
Dr. Debbie Bennett
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • deep learning
  • radiomics
  • machine learning
  • breast cancer
  • breast cancer risk
  • digital mammography
  • breast tomosynthesis
  • breast MRI
  • breast ultrasound

Published Papers (4 papers)

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Research

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Article
Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism
Tomography 2022, 8(5), 2411-2425; https://doi.org/10.3390/tomography8050200 - 28 Sep 2022
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Abstract
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning [...] Read more.
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Cancer Screening)
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Article
Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer
Tomography 2022, 8(1), 131-141; https://doi.org/10.3390/tomography8010011 - 05 Jan 2022
Cited by 4 | Viewed by 1772
Abstract
Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with [...] Read more.
Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80–98% and a specificity of 80%, 88%, and 76–92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872–0.967, p = 0.036–0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Cancer Screening)
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Review

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Review
A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms
Tomography 2022, 8(6), 2874-2892; https://doi.org/10.3390/tomography8060241 - 06 Dec 2022
Viewed by 159
Abstract
Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in [...] Read more.
Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Cancer Screening)
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Review
Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review
Tomography 2022, 8(6), 2784-2795; https://doi.org/10.3390/tomography8060232 - 21 Nov 2022
Viewed by 459
Abstract
Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to [...] Read more.
Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Cancer Screening)
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