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Breast Cancer Metastasis, Diagnostic and Therapeutic Approaches 2022

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (30 October 2022) | Viewed by 9527

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Special Issue Editor

Dr. Ewa Grzybowska

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Guest Editor

Maria Sklodowska-Curie National Research Institute of Oncology, Roentgena 5, 02-781 Warsaw, Poland
Interests: breast cancer; cell migration; cell adhesion; metastasis; circulating tumor cells; HAX1; entosis; RNA-binding proteins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer is the most frequently diagnosed tumor and the leading cause of cancer death in women. Metastatic breast cancer is incurable, and metastasis is the main cause of mortality. While strategies targeting the primary tumor are very effective, the systemic treatment of metastatic disease is less successful. Disease heterogeneity, long latency periods, and genetic/phenotypic differences between primary tumor and metastatic lesions present challenges to the diagnostic and therapeutic efforts. New approaches encompassing single-cell analysis, liquid biopsy, exosomal load, and tumor microenvironment characteristics should expand our knowledge and give us more tools to combat metastatic disease.

This open-access Special Issue will bring together original research and review articles on breast cancer metastasis, its biology and diagnostics, and therapeutic approaches to combat it. It will provide a platform to share new discoveries and technical developments in the field of breast cancer research for the development of the next generation of anti-metastasis treatments.

Topics of this Special Issue include, but are not limited to:

Biology of metastasis (signaling pathways, new markers, molecular characteristics of the tumor);
Tumor dissemination research (triggering factors, intravasation, extravasation, study of dormant and disseminated cells);
New diagnostic approaches (CTC, ctDNA, exosomes, microenvironment);
New therapeutic approaches, targeted therapies, translational studies.

Dr. Ewa A. Grzybowska
Guest Editor

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. Diagnostics 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 2600 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

metastatic breast cancer
heterogeneity
tumor dissemination
metastatic recurrence
targeted therapy

Published Papers (5 papers)

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Research

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15 pages, 1790 KiB

Open AccessArticle

Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers

by Ahmed A. Alsheikhy, Yahia Said, Tawfeeq Shawly, A. Khuzaim Alzahrani and Husam Lahza

Diagnostics 2022, 12(11), 2863; https://doi.org/10.3390/diagnostics12112863 - 18 Nov 2022

Cited by 3 | Viewed by 1479

Abstract

Breast cancer is considered one of the deadliest diseases in women. Due to the risk and threat it poses, the world has agreed to hold a breast cancer awareness day in October, encouraging women to perform mammogram inspections. This inspection may prevent breast-cancer-related deaths or reduce the death rate. The identification and classification of breast cancer are challenging tasks. The most commonly known procedure of breast cancer detection is performed by using mammographic images. Recently implemented algorithms suffer from generating accuracy below expectations, and their computational complexity is high. To resolve these issues, this paper proposes a fully automated biomedical diagnosis system of breast cancer using an AlexNet, a type of Convolutional Neural Network (CNN), and multiple classifiers to identify and classify breast cancer. This system utilizes a neuro-fuzzy method, a segmentation algorithm, and various classifiers to reach a higher accuracy than other systems have achieved. Numerous features are extracted to detect and categorize breast cancer. Three datasets from Kaggle were tested to validate the proposed system. The performance evaluation is performed with quantitative and qualitative accuracy, precision, recall, specificity, and F-score. In addition, a comparative assessment is performed between the proposed system and some works of literature. This assessment shows that the presented algorithm provides better classification results and outperforms other systems in all parameters. Its average accuracy is over 98.6%, while other metrics are more than 98%. This research indicates that this approach can be applied to assist doctors in diagnosing breast cancer correctly. Full article

(This article belongs to the Special Issue Breast Cancer Metastasis, Diagnostic and Therapeutic Approaches 2022)

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10 pages, 835 KiB

Open AccessArticle

Prognostic Comparison between Oncotype DX^® and a 23-Gene Classifier, RecurIndex^®, on the Taiwan Breast Cancer Population

by Chuan-Hsun Chang, Po-Sheng Yang, Chia-Ming Hsieh, Ting-Hao Chen, Skye Hung-Chun Cheng, Cheng-En Yang and Chiun-Sheng Huang

Diagnostics 2022, 12(11), 2850; https://doi.org/10.3390/diagnostics12112850 - 17 Nov 2022

Viewed by 1325

Abstract

The applicability of the Oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA) recurrence score (RS) in Asian populations is unclear. A 23-gene classifier, RecurIndex^® (Amwise Diagnostics, Pte. Ltd., Singapore), has been developed based on the gene expression profiles of early-stage breast cancer patients of ethnic Han Chinese population in Taiwan. This study aimed to compare the performance of the Oncotype DX^® RS with the RecurIndex^® recurrence index (RI) for predicting relapse-free survival. Therefore, we calculated both the RI and RS for 110 early stage breast cancer patients, with the cut-off value for high-risk recurrence set at 26 and 29 for the RS and the RI, respectively. With relapse-free interval (RFI) as the primary endpoint, the concordance between RS and RI was 78.2% (Kappa value = 0.297). For a median follow-up interval of 27 months, there was a statistically significant difference in RFI between the high- and low-risk groups defined by the RI (p = 0.04) but not between risk groups defined by the RS (p = 0.66). In conclusion, whereas there was high concordance between the RecurIndex^® RI and the Oncotype DX RS, the current data showed that the RI had a better discrimination for recurrence risk than the RS. Subsequent studies with larger sample sizes will be needed to confirm the superiority of the RI over the RS in the Asian population. Full article

(This article belongs to the Special Issue Breast Cancer Metastasis, Diagnostic and Therapeutic Approaches 2022)

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14 pages, 3040 KiB

Open AccessArticle

BUViTNet: Breast Ultrasound Detection via Vision Transformers

by Gelan Ayana and Se-woon Choe

Diagnostics 2022, 12(11), 2654; https://doi.org/10.3390/diagnostics12112654 - 01 Nov 2022

Cited by 20 | Viewed by 2944

Abstract

Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of incorporating more global information than CNNs at lower layers, and their skip connections are more powerful than those of CNNs, which endows ViTs with superior performance. However, the effectiveness of ViTs in breast ultrasound imaging has not yet been investigated. Here, we present BUViTNet breast ultrasound detection via ViTs, where ViT-based multistage transfer learning is performed using ImageNet and cancer cell image datasets prior to transfer learning for classifying breast ultrasound images. We utilized two publicly available ultrasound breast image datasets, Mendeley and breast ultrasound images (BUSI), to train and evaluate our algorithm. The proposed method achieved the highest area under the receiver operating characteristics curve (AUC) of 1 ± 0, Matthew’s correlation coefficient (MCC) of 1 ± 0, and kappa score of 1 ± 0 on the Mendeley dataset. Furthermore, BUViTNet achieved the highest AUC of 0.968 ± 0.02, MCC of 0.961 ± 0.01, and kappa score of 0.959 ± 0.02 on the BUSI dataset. BUViTNet outperformed ViT trained from scratch, ViT-based conventional transfer learning, and CNN-based transfer learning in classifying breast ultrasound images (p < 0.01 in all cases). Our findings indicate that improved transformers are effective in analyzing breast images and can provide an improved diagnosis if used in clinical settings. Future work will consider the use of a wide range of datasets and parameters for optimized performance. Full article

(This article belongs to the Special Issue Breast Cancer Metastasis, Diagnostic and Therapeutic Approaches 2022)

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10 pages, 966 KiB

Open AccessArticle

Residual Tumor Patterns of Breast Cancer on MRI after Neo-Adjuvant Chemotherapy: Impact on Clinicopathologic Factors and Prognosis

by Yoon Jin Cha, Na Lae Eun, Dooreh Kim, Soong June Bae, Sung Gwe Ahn, Joon Jeong, Woo-Chan Park, Yangkyu Lee and Chang Ik Yoon

Diagnostics 2022, 12(10), 2294; https://doi.org/10.3390/diagnostics12102294 - 23 Sep 2022

Viewed by 1310

Abstract

(1) Background: Residual breast cancer after neoadjuvant chemotherapy (NAC) could have a variable image pattern on a follow-up breast magnetic resonance image (MRI). In this study, we compared the clinical outcome of breast cancer patients with different residual tumor patterns (RTP) on a breast MRI after NAC. (2) Methods: A total of 91 patients with breast cancer who received NAC and subsequent curative surgery were selected. All included patient had residual breast cancer after NAC and showed a partial response on a breast MRI. Pre- and post-treatment were reviewed by an experienced radiologist to evaluate different RTP, and classified into two groups: concentric and scattered patterns. The clinicopathologic parameters and survival outcomes [recurrence-free survival (RFS) and distant metastasis-free survival (DMFS)] were analyzed according to different RTP. (3) Results: Patients with a scattered pattern had a larger extent of pre-treated non-mass enhancement and more frequently received total mastectomy. With a median follow-up period of 37 months, RTP were not significantly associated with RFS or DMFS. (4) Conclusions: In the patients with residual breast cancer after NAC, RTP on an MRI had no effect on the patients’ clinical outcome. The curative resection of the tumor bed and securing the negative resection margins appear to be important in the treatment of patients with residual breast cancer after NAC. Full article

(This article belongs to the Special Issue Breast Cancer Metastasis, Diagnostic and Therapeutic Approaches 2022)

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Other

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17 pages, 3039 KiB

Open AccessSystematic Review

The Role of miRNAs in the Prognosis of Triple-Negative Breast Cancer: A Systematic Review and Meta-Analysis

by Talita Araújo B. da S. Santana, Larissa de Oliveira Passamai, Felipe Silva de Miranda, Thaiz Ferraz Borin, Grasiely Faccin Borges, Wilson Barros Luiz and Luciene Cristina Gastalho Campos

Diagnostics 2023, 13(1), 127; https://doi.org/10.3390/diagnostics13010127 - 30 Dec 2022

Cited by 5 | Viewed by 1749

Abstract

Breast cancer is one of the most common malignancies among women around the world. The basal or triple-negative subtype (TNBC) is a heterogeneous group of tumors, characterized by its aggressive and metastatic nature, with low survival and worse prognosis. Research on genetic biomarkers, such as microRNAs (miRs) in TNBC, demonstrate their relevance in the prognosis of the disease. Therefore, the objective of this research was to verify the role of miRs in the prognosis of TNBC. A search was carried out in the PubMed (MEDLINE), Web of Science, and Scopus databases, with articles in the English language from 2010 to 2022. Only articles that analyzed the role of miRNAs in the prognosis of TNBC and that met the criteria of the MOOSE method were included. For the preparation and planning of this systematic review, a PRISMA checklist and the MOOSE method were used. The Newcastle–Ottawa Scale was used to analyze the quality of the included studies. The excluded criteria considered were: (1) studies that presented duplication in the databases; (2) reviews of the literature, clinical case reports, meta-analyses, conference abstracts, letters to the editor, theses, dissertations, and book chapters; (3) studies that stratified only women diagnosed with other subtypes of breast cancer subtypes; (4) experiments without a control or comparison group. After the bibliographic survey of the 2.274 articles found, 43 articles met the inclusion criteria, totaling 5421 patients with TNBC analyzed for this review. Six miRs (miR-155, miR-21, miR-27a/b/, miR-374a/b, miR-30a/c/e, and miR-301a) were included in the meta-analysis. A low expression of miR-155 was associated with reduced overall survival (OS) (HR: 0.68, 95% CI: 0.58–0.81). A high expression of miR-21 was a predictor of OS reduction (HR: 2.56; 95% CI: 1.49–4.40). In addition, high levels of miR-27a/b and miR-301a/b were associated with lower OS, while the decreased expression levels of miR-30 and miR-374a/b were associated with worse relapse-free survival (RFS) and shorter disease-free survival (DFS), respectively. The present study revealed that miRs play essential roles in the development of metastases, in addition to acting as suppressors of the disease, thus improving the prognosis of TNBC. However, the clinical application of these findings has not yet been investigated. Full article

(This article belongs to the Special Issue Breast Cancer Metastasis, Diagnostic and Therapeutic Approaches 2022)

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