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Medicina
Special Issues
Breast Cancer: Early Diagnosis
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Breast Cancer: Early Diagnosis

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 7339

Special Issue Editor

Dr. Marco Pellicciaro

E-Mail Website
Guest Editor
Breast Unit, Department of Surgical Science, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: early diagnosis of breast cancer

Special Issue Information

Dear Colleagues,

Breast cancer is the most common neoplasia in females, affecting around one out of nine women during their life. Over the years, the diagnostic pathways have changed from a palpable evident breast lesion to what is nowadays considered as locally advanced breast cancer in non-palpable lesions. The implementation of breast cancer screening programs and the advent of digital mammography have led to the increased detection of early-stage breast cancers. The progress of treatments and higher incidence of early breast cancer diagnosis have led to a significantly reduction in mastectomy and axillary lymph nodes dissection, with increasing use of minimally invasive surgery. Despite the use of increasingly conservative treatments, survival rates and disease-free survival have nevertheless increased. The treatments used have transitioned from mastectomy to lumpectomy for breast surgery and from lymphadenectomy to lymph node sentinel biopsy, until its omission in some cases of early-stage cancer.

On the other hand, diagnosis at increasingly earlier stages has led to the diagnosis of small carcinomas in situ, and the treatment of these has been considered by some investigators to be overtreatment.

The aim of this Special issue is to evaluate the appropriate diagnostic pathways and option treatments for early breast cancer. Moreover, of interest for the Special Issue are the cases of omission of the sentinel lymph node, axillary dissection or omission of surgical intervention and any optional treatments.

The Special Issue will consider the following types of articles: editorials, review articles, original articles, case reports if associated with literature reviews and rapid communications.

Dr. Marco Pellicciaro
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. Medicina is an international peer-reviewed open access monthly 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 2200 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

  • early breast cancer
  • conservative breast surgery
  • omitting sentinel lymph node biopsy
  • sentinel lymph node biopsy
  • vacuum-assisted breast lesion excision
  • in situ breast cancer

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Published Papers (2 papers)

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25 pages, 12232 KiB  
Article
Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer
by Khanis Farhana Tuly, Md. Bayazid Hossen, Md. Ariful Islam, Md. Kaderi Kibria, Md. Shahin Alam, Md. Harun-Or-Roshid, Anjuman Ara Begum, Sohel Hasan, Rashidul Alam Mahumud and Md. Nurul Haque Mollah
Medicina 2023, 59(10), 1705; https://doi.org/10.3390/medicina59101705 - 24 Sep 2023
Cited by 5 | Viewed by 3994
Abstract
Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the [...] Read more.
Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study’s findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC. Full article
(This article belongs to the Special Issue Breast Cancer: Early Diagnosis)
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10 pages, 3727 KiB  
Case Report
The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature
by Diana Maria Chiorean, Melinda-Ildiko Mitranovici, Maria Cezara Mureșan, Corneliu-Florin Buicu, Raluca Moraru, Liviu Moraru, Titiana Cornelia Cotoi, Ovidiu Simion Cotoi, Adrian Apostol, Sabin Gligore Turdean, Claudiu Mărginean, Ion Petre, Ioan Emilian Oală, Zsuzsanna Simon-Szabo, Viviana Ivan, Ancuța Noela Roșca and Havva Serap Toru
Medicina 2023, 59(4), 672; https://doi.org/10.3390/medicina59040672 - 28 Mar 2023
Cited by 2 | Viewed by 2738
Abstract
Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To [...] Read more.
Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1′s expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient’ s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO—IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters. Full article
(This article belongs to the Special Issue Breast Cancer: Early Diagnosis)
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