Application of Ultrasound in Breast Cancer

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 4209

Special Issue Editor


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Guest Editor
The Institute of Cancer Research and Royal Marsden Hospital NHS Foundation Trust, Radiotherapy and Imaging, London SM2 5PT, UK
Interests: radiation oncology; breast cancer radiotherapy; development of various novel ultrasonic imaging techniques

Special Issue Information

Dear Colleagues,

As ultrasound rapidly continues to develop far beyond anatomical imaging to a state-of-the-art functional and molecular imaging modality, new opportunities for both diagnosis and the monitoring of responses to therapy in breast cancer are opening up. The exciting new paradigm of ultrasound microvascular imaging, which encompasses techniques such as super-resolution imaging, acoustic angiography and micro-Doppler imaging, means we can now probe the tumour microenvironment using ultrasound. Recent innovations in ultrasound-contrast nanoagents, which can be targeted to tumour cell receptors, broaden the scope for the development of highly sensitive diagnostic tools and for the characterization and spatial mapping of tumour biology on a molecular level. Elastography and ultrasonic tissue characterisation hold the ability to interrogate the evolving structure of progressive or regressive cancer. In addition to improving the detection and management of breast cancer clinically, all of these techniques can be used preclinically to support the development of cancer therapies. Importantly, ultrasound can also be used as a cancer therapeutic itself; a combination of microbubble contrast agents with low-intensity ultrasound is a promising new technology to both aid drug delivery and increase tumour radiosensitivity. Ablative ultrasound therapies and hyperthermia can also be used to complement drug and radiation treatments.

This Special Issue aims to capture a cross section of the exciting new possibilities ultrasound offers for better detection, diagnosis, assessment of treatment response, and treatment of breast cancer. We are interested in the development and validation of novel ultrasound technologies applied to breast cancer, as well as the use of ultrasound to elucidate new understandings of breast cancer biology, the tumour microenvironment, and the mechanisms underlying response or resistance to cancer therapies.

Dr. Emma J. Harris
Guest Editor

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Keywords

  • ultrasound
  • functional imaging
  • elastography
  • molecular imaging
  • breast cancer
  • ultrasound therapy
  • nanoparticles
  • drug delivery
  • vascular imaging
  • tumour microenvironment

Published Papers (2 papers)

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Research

19 pages, 6540 KiB  
Article
Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images
by Dinusha Veluponnar, Lisanne L. de Boer, Freija Geldof, Lynn-Jade S. Jong, Marcos Da Silva Guimaraes, Marie-Jeanne T. F. D. Vrancken Peeters, Frederieke van Duijnhoven, Theo Ruers and Behdad Dashtbozorg
Cancers 2023, 15(6), 1652; https://doi.org/10.3390/cancers15061652 - 08 Mar 2023
Cited by 2 | Viewed by 2057
Abstract
There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low [...] Read more.
There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery. Full article
(This article belongs to the Special Issue Application of Ultrasound in Breast Cancer)
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14 pages, 2286 KiB  
Article
Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study
by Yingying Jia, Ruichao Wu, Xiangyu Lu, Ying Duan, Yangyang Zhu, Yide Ma and Fang Nie
Cancers 2023, 15(3), 838; https://doi.org/10.3390/cancers15030838 - 29 Jan 2023
Cited by 3 | Viewed by 1827
Abstract
This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. [...] Read more.
This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort (n = 298) and internal validation (IV) cohort (n = 98). Patients from hospital 2 (n = 98) were in the external validation (EV) cohort. TIL levels were confirmed by pathological results. Five different DL models were trained for predicting TIL levels in BC using US images from the training cohort and validated on the IV and EV cohorts. The overall best-performing DL model, the attention-based DenseNet121, achieved an AUC of 0.873, an accuracy of 79.5%, a sensitivity of 90.7%, a specificity of 65.9%, and an F1 score of 0.830 in the EV cohort. In addition, the stratified analysis showed that the DL models had good discrimination performance of TIL levels in each of the molecular subgroups. The DL models based on US images of BC patients hold promise for non-invasively predicting TIL levels and helping with individualized treatment decision-making. Full article
(This article belongs to the Special Issue Application of Ultrasound in Breast Cancer)
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