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Diagnostics
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Advances in Diagnosis and Prognosis of Breast Cancer
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Advances in Diagnosis and Prognosis of Breast Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1147

Special Issue Editor

Dr. Domen Ribnikar

E-Mail Website
Guest Editor
Division of Medical Oncology, Institute of Oncology Ljubljana, Faculty of Medicine, University of Ljubljana, Zaloska Cesta 2, 1000 Ljubljana, Slovenia
Interests: genomics; cancer biomarkers; cancer biology; gene expression; tumor biology; chemotherapy; targeted therapy; metastasis

Special Issue Information

Dear Colleagues,

Breast cancer remains the most prevalent malignancy in women and its incidence continues to increase worldwide. In the last decade, meaningful insights into the biology of breast cancer have been achieved, and therefore significant advances in locoregional and systemic treatment have been made.

The latest diagnostic techniques include advanced imaging modalities such as MRI, PET-CT, and ultrasound-guided biopsy. In particular, there is a focus on the role of molecular biomarkers in early detection and personalized medicine. In addition, with the advent of genetic testing, our understanding of the risk of breast cancer has evolved significantly.

Exploring classical clinical–pathological and genomic prognostic parameters can more precisely facilitate in predicting the likelihood of distant disease recurrence and overall survival.

This Special Issue aims to enhance our knowledge regarding the latest advancements in the field of breast cancer diagnosis and prognosis. Original research articles, reviews and other papers are welcome. We look forward to receiving your valuable submissions. 

Dr. Domen Ribnikar
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

  • breast cancer
  • gene expression profiling in breast cancer
  • cancer biomarkers
  • tumor biology
  • prediction
  • prognosis
  • systemic treatment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 1948 KiB  
Article
Comparative Study of AI Modes in Ultrasound Diagnosis of Breast Lesions
by Yu-Ting Hong, Zi-Han Yu and Chen-Pin Chou
Diagnostics 2025, 15(5), 560; https://doi.org/10.3390/diagnostics15050560 - 26 Feb 2025
Viewed by 275
Abstract
Objectives: This study evaluated the diagnostic performance of the S-Detect ultrasound system’s three selectable AI modes—high-sensitivity (HSe), high-accuracy (HAc), and high-specificity (HSp)—for breast lesion diagnosis, comparing their performance in a clinical setting. Methods: This retrospective analysis evaluated 260 breast lesions from ultrasound images [...] Read more.
Objectives: This study evaluated the diagnostic performance of the S-Detect ultrasound system’s three selectable AI modes—high-sensitivity (HSe), high-accuracy (HAc), and high-specificity (HSp)—for breast lesion diagnosis, comparing their performance in a clinical setting. Methods: This retrospective analysis evaluated 260 breast lesions from ultrasound images of 232 women (mean age: 50.2 years) using the S-Detect system. Each lesion was analyzed under the HSe, HAc, and HSp modes. The study employed ROC curve analysis to comprehensively compare the diagnostic performance of the AI modes against radiologist diagnoses. Subgroup analyses focused on the age (<45, 45–55, >55 years) and lesion size (<1 cm, 1–2 cm, >2 cm). Results: Among the 260 lesions, 73% were identified as benign and 27% as malignant. Radiologists achieved a sensitivity of 98.6%, specificity of 64.2%, and accuracy of 73.5%. The HSe mode exhibited the highest sensitivity at 95.7%. The HAc mode excelled with the highest accuracy (86.2%) and positive predictive value (71.3%), while the HSp mode had the highest specificity at 95.8%. In the age-based subgroup analyses, the HAc mode consistently showed the highest area under the curve (AUC) across all categories. The HSe mode achieved the highest AUC (0.726) for lesions smaller than 1 cm. In the case of lesions sized 1–2 cm and larger than 2 cm, the HAc mode showed the highest AUCs of 0.906 and 0.776, respectively. Conclusions: The S-Detect HSe mode matches radiologists’ performance. Alternative modes provide sensitivity and specificity adjustments. The patient age and lesion size influence the diagnostic performance across all S-Detect modes. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Prognosis of Breast Cancer)
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Figure 1

Figure 1
<p>An AI-generated region of interest (ROI) was delineated around the mass’s border. S-Detect automatically extracted and displayed ultrasound features based on the ultrasound BI-RADS lexicon. The final determination based on the high-accuracy (HAc) mode was categorized as either “possibly benign” or “possibly malignant” (arrow).</p> Full article ">Figure 2
<p>A 61-year-old woman diagnosed with invasive breast cancer. (<b>a</b>) A greyscale breast ultrasound serving as an example of how AI software (V2) can evaluate a suspicious lesion through three AI modes. (<b>b</b>) The S-detect system on the ultrasound machine provides three distinct AI modes: high-sensitivity (HSe), high-accuracy (HAc), and high-specificity (HSp). (<b>c</b>) Utilizing AI capabilities, the lesion of interest is automatically outlined when a sonographer places a marker within the targeted area.</p> Full article ">Figure 2 Cont.
<p>A 61-year-old woman diagnosed with invasive breast cancer. (<b>a</b>) A greyscale breast ultrasound serving as an example of how AI software (V2) can evaluate a suspicious lesion through three AI modes. (<b>b</b>) The S-detect system on the ultrasound machine provides three distinct AI modes: high-sensitivity (HSe), high-accuracy (HAc), and high-specificity (HSp). (<b>c</b>) Utilizing AI capabilities, the lesion of interest is automatically outlined when a sonographer places a marker within the targeted area.</p> Full article ">Figure 3
<p>Radiologists and three AI modes (high-sensitivity (HSe), high-accuracy (HAc), and high-specificity (HSp)) had varying diagnostic performance in terms of sensitivity and specificity for breast ultrasound mass lesions. The HSe mode closely aligned with radiologists’ performance.</p> Full article ">Figure 4
<p>Diagnostic performance of the three AI modes in breast lesion analysis stratified by the lesion size (&lt;1 cm, 1–2 cm, and &gt;2 cm). Performance was assessed using the area under the curve (AUC).</p> Full article ">Figure 5
<p>Diagnostic performance of the three AI modes in breast lesion analysis stratified by patient age groups (&lt;45 years, 45–55 years, and &gt;55 years). Performance was assessed using the area under the curve (AUC).</p> Full article ">
13 pages, 2733 KiB  
Article
Radiomic Analysis of Magnetic Resonance Imaging for Breast Cancer with TP53 Mutation: A Single Center Study
by Jung Ho Park, Lyo Min Kwon, Hong Kyu Lee, Taeryool Koo, Yong Joon Suh, Mi Jung Kwon and Ho Young Kim
Diagnostics 2025, 15(4), 428; https://doi.org/10.3390/diagnostics15040428 - 10 Feb 2025
Viewed by 432
Abstract
Background: Radiomics is a non-invasive and cost-effective method for predicting the biological characteristics of tumors. In this study, we explored the association between radiomic features derived from magnetic resonance imaging (MRI) and genetic alterations in patients with breast cancer. Methods: We [...] Read more.
Background: Radiomics is a non-invasive and cost-effective method for predicting the biological characteristics of tumors. In this study, we explored the association between radiomic features derived from magnetic resonance imaging (MRI) and genetic alterations in patients with breast cancer. Methods: We reviewed electronic medical records of patients with breast cancer patients with available targeted next-generation sequencing data available between August 2018 and May 2021. Substraction imaging of T1-weighted sequences was utilized. The tumor area on MRI was segmented semi-automatically, based on a seeded region growing algorithm. Radiomic features were extracted using the open-source software 3D slicer (version 5.6.1) with PyRadiomics extension. The association between genetic alterations and radiomic features was examined. Results: In total, 166 patients were included in this study. Among the 50 panel genes analyzed, only TP53 mutations were significantly associated with radiomic features. Compared with TP53 wild-type tumors, TP53 mutations were associated with larger tumor size, advanced stage, negative hormonal receptor status, and HER2 positivity. Tumors with TP53 mutations exhibited higher values for Gray Level Non-Uniformity, Dependence Non-Uniformity, and Run Length Non-Uniformity, and lower values for Sphericity, Low Gray Level Emphasis, and Small Dependence Low Gray Level emphasis compared to TP53 wild-type tumors. Six radiomic features were selected to develop a composite radiomics score. Receiver operating characteristic curve analysis showed an area under the curve of 0.786 (95% confidence interval, 0.719–0.854; p < 0.001). Conclusions: TP53 mutations in breast cancer can be predicted using MRI-derived radiomic analysis. Further research is needed to assess whether radiomics can help guide treatment decisions in clinical practice. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Prognosis of Breast Cancer)
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Figure 1

Figure 1
<p>Radiomics work-flow of the study. (<b>A</b>) Substraction images were loaded. (<b>B</b>) Tumor area was segmented semi-automatically. (<b>C</b>) Radiomic features were extracted using radiomics module. (<b>D</b>) Logistic regression using radiomics features. (<b>E</b>) <span class="html-italic">TP53</span> mutations were predicted using ROC curve analysis.</p> Full article ">Figure 2
<p>Flow-diagram of the patient selection process.</p> Full article ">Figure 3
<p>Mutational profiles of the tumors shown by Oncoprint.</p> Full article ">Figure 4
<p>Three-dimensional views of the segmented tumors with <span class="html-italic">TP53</span> mutations.</p> Full article ">
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