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Diagnostics
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Breast Cancer Theranostics
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Breast Cancer Theranostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (16 December 2022) | Viewed by 31391

Special Issue Editors

Dr. Kwangsoon Kim

E-Mail Website
Guest Editor
Department of Surgery, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Interests: thyroid; thyroid cancer; surgery
Special Issues, Collections and Topics in MDPI journals
Dr. Chang Ik Yoon

E-Mail Website
Guest Editor
Department of Surgery, College of Medicine, the Catholic University of Korea, Seoul 06591, Republic of Korea
Interests: breast cancer; surgical oncology; biomarker; metastasis

Special Issue Information

Dear Colleagues,

Breast cancer is the most common invasive cancer in the world and the second leading cause of cancer deaths in females. The survival rate of breast cancer patients has improved due to the increase in screening programs and the emergence of novel drugs. However, the survival outcomes in metastatic breast cancer are still poor. The heterogeneity of breast cancer poses a challenge to its management. Various factors affect the prognosis, aggressive phenotype, and treatment response of breast cancer.

A major challenge in the management of breast cancer is individual variability among patients. Recently, increased understanding of breast cancer biology had led to the identification of specific targets, expressed on cancer cells, that can be used for diagnosis and therapy at the molecular level of a specific individual, thus improving the therapeutic outcome. Theranostics is the combination of the diagnostic and therapeutic agents targeted at the same biomarker such as HER2. The advantage of the theranostic approach is that it identifies individuals who would benefit from a specific treatment and, thus, prevent the implementation of potentially futile treatments. This Special Issue of Diagnostics aims to provide an overview of the current developments in theranostics for breast cancer.

Dr. Kwangsoo Kim
Dr. Chang Ik Yoon
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. 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.

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Published Papers (5 papers)

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Research

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21 pages, 5059 KiB  
Article
Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images
by Md. Sakib Hossain Shovon, Md. Jahidul Islam, Mohammed Nawshar Ali Khan Nabil, Md. Mohimen Molla, Akinul Islam Jony and M. F. Mridha
Diagnostics 2022, 12(11), 2825; https://doi.org/10.3390/diagnostics12112825 - 16 Nov 2022
Cited by 25 | Viewed by 3958
Abstract
Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help [...] Read more.
Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called ‘HE-HER2Net’ has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method. Full article
(This article belongs to the Special Issue Breast Cancer Theranostics)
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14 pages, 1656 KiB  
Article
Targeted Sequencing of Germline Breast Cancer Susceptibility Genes for Discovering Pathogenic/Likely Pathogenic Variants in the Jakarta Population
by Sonar Soni Panigoro, Rafika Indah Paramita, Kristina Maria Siswiandari and Fadilah Fadilah
Diagnostics 2022, 12(9), 2241; https://doi.org/10.3390/diagnostics12092241 - 16 Sep 2022
Viewed by 2507
Abstract
Germline predisposition plays an important role in breast cancer. Different ethnic populations need respective studies on cancer risks pertinent to germline variants. We aimed to discover the pathogenic and likely pathogenic variants (P/LP-Vs) of germline breast cancer susceptibility genes and to evaluate their [...] Read more.
Germline predisposition plays an important role in breast cancer. Different ethnic populations need respective studies on cancer risks pertinent to germline variants. We aimed to discover the pathogenic and likely pathogenic variants (P/LP-Vs) of germline breast cancer susceptibility genes and to evaluate their correlation with the clinical characteristics in Jakarta populations. The pure DNA was extracted from the blood buffy coat, using reagents from the QIAamp DNA Mini Kit® (Qiagen, Hilden, Germany). The DNA libraries were prepared using the TargetRich™ Hereditary Cancer Panel (Kailos Genetics®, Huntsville, AL, USA). The barcoded DNA libraries were sequenced using the Illumina NextSeq 500 platform. In-house bioinformatics pipelines were used to analyze the gene variants. We identified 35 pathogenic and likely pathogenic (P/LP-Vs) variants (28 frameshift, 5 nonsense, and 2 splice-site variants). The P/LP-Vs group was statistically significantly different in luminal B status (p < 0.05) compared with the non-P/LP-Vs group. The P/LP-Vs found both in BRCA1/2 genes and non-BRCA genes may increase the risk of breast cancer and alter drug responses. The screening of multigene variants is suggested, rather than BRCA testing only. Prior knowledge of the germline variants status is important for optimal breast cancer diagnosis and optimal therapy. Full article
(This article belongs to the Special Issue Breast Cancer Theranostics)
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Review

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13 pages, 300 KiB  
Review
Theranostics for Triple-Negative Breast Cancer
by Hyeryeon Choi and Kwangsoon Kim
Diagnostics 2023, 13(2), 272; https://doi.org/10.3390/diagnostics13020272 - 11 Jan 2023
Cited by 19 | Viewed by 3657
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor prognosis. Current endocrine therapy or anti HER-2 therapy is not available for these patients. Chemotherapeutic treatment response varies among patients due to the disease heterogeneity. To overcome these challenges, theranostics [...] Read more.
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor prognosis. Current endocrine therapy or anti HER-2 therapy is not available for these patients. Chemotherapeutic treatment response varies among patients due to the disease heterogeneity. To overcome these challenges, theranostics for treating TNBC have been widely investigated. Anticancer material conjugated nanoparticles with target-binding ligand and tracer agents enable simultaneous drug delivery and visualization of the lesion with minimal off-target toxicity. In this review, we summarize recently FDA-approved targeted therapies for TNBC, such as poly-ADP-ribose polymerase (PARP) inhibitors, check point inhibitors, and antibody-drug conjugates. Particularly, novel theranostic approaches including lipid-based, polymer-based, and carbon-based nanocarriers are discussed, which can provide basic overview of nano-therapeutic modalities in TNBC diagnosis and treatment. Full article
(This article belongs to the Special Issue Breast Cancer Theranostics)
18 pages, 786 KiB  
Review
Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine
by Aisyah Fitriannisa Prawiningrum, Rafika Indah Paramita and Sonar Soni Panigoro
Diagnostics 2022, 12(12), 2981; https://doi.org/10.3390/diagnostics12122981 - 28 Nov 2022
Cited by 14 | Viewed by 3823
Abstract
Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried [...] Read more.
Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein–epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein–epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution. Full article
(This article belongs to the Special Issue Breast Cancer Theranostics)
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Other

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26 pages, 4780 KiB  
Systematic Review
Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction
by Maged Nasser and Umi Kalsom Yusof
Diagnostics 2023, 13(1), 161; https://doi.org/10.3390/diagnostics13010161 - 3 Jan 2023
Cited by 106 | Viewed by 16402
Abstract
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early [...] Read more.
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients’ survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area. Full article
(This article belongs to the Special Issue Breast Cancer Theranostics)
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