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Medicina
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New Developments in Diagnosis and Management of Breast Cancer
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New Developments in Diagnosis and Management of Breast Cancer

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

Deadline for manuscript submissions: 25 April 2026 | Viewed by 12733

Special Issue Editors

Prof. Dr. Daliborca Cristina Vlad

E-Mail Website
Guest Editor
Department of Pharmacology ''Victor Babes'' University of Medicine and Pharmacy Timisoara, Romania
Interests: clinical and experimental pharmacology; ethnopharmacology; immunology; immunopathology; clinical trials
Dr. Ionut Marcel Cobec

E-Mail Website
Guest Editor
1. ANAPATMOL Research Center, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
2. Clinic of Obstetrics and Gynecology, Klinikum Freudenstadt, 72250 Freudenstadt, Germany
Interests: breast cancer; therapy; personalized therapy; gynecologic oncology; gynecologic cancers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer continues to be the most prevalent malignancy among women and remains the leading cancer diagnosis worldwide. Despite the significant progress made, it presents an ongoing clinical challenge due to its biological heterogeneity and variable prognosis.

The landscape of breast cancer management and therapy is rapidly evolving, driven by the advent of innovative diagnostic tools and therapeutic strategies. Modern advancements in molecular profiling, targeted therapies, and precision medicine have significantly improved disease outcomes and enhanced overall survival rates. These developments underscore the necessity for a personalized approach to both diagnosis and treatment, tailored to the unique biological characteristics of each patient’s case of the disease.

However, the dynamic nature of this field necessitates continuous research to address existing gaps and explore novel methodologies. This Special Issue will gather groundbreaking studies and comprehensive reviews that illuminate the latest trends, innovations, and scientific breakthroughs in the diagnosis and management of breast cancer. By fostering knowledge dissemination, this Special Issue will contribute meaningfully to the advancement of clinical practice and future research directions.

It is with great respect and anticipation that I invite you to submit your original research articles or systematic reviews to this Special Issue. Your contributions will not only serve as a testament to the progress already made in this critical field but will also act as a foundation for shaping future innovations and improving patient outcomes in breast cancer care.

I look forward to receiving your contributions.

Prof. Dr. Daliborca Cristina Vlad
Dr. Ionut Marcel Cobec
Guest Editors

Manuscript Submission Information

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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

  • breast cancer
  • diagnosis
  • management
  • therapy
  • targeted therapy
  • triple-negative breast cancer
  • screening
  • overall survival
  • prognosis
  • immunotherapy
  • surgery
  • new techniques
  • staging

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Published Papers (9 papers)

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Research

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14 pages, 1113 KB  
Article
Breast Lesions of Uncertain Malignant Potential and Risk of Breast Cancer Development: A Single-Center Experience on 10,531 Consecutive Biopsies
by Maria Orsaria, Alessandro Mangogna, Massimo Bertoli, Carla Di Loreto and Enrico Pegolo
Medicina 2025, 61(10), 1877; https://doi.org/10.3390/medicina61101877 - 20 Oct 2025
Viewed by 384
Abstract
Background and Objectives: Breast lesions of uncertain malignant potential identified on biopsy, known as “B3 lesions,” constitute a significant portion of diagnoses in numerous published studies. These lesions are associated with a variable risk of coinciding malignant tumors, and current guidelines recommend [...] Read more.
Background and Objectives: Breast lesions of uncertain malignant potential identified on biopsy, known as “B3 lesions,” constitute a significant portion of diagnoses in numerous published studies. These lesions are associated with a variable risk of coinciding malignant tumors, and current guidelines recommend complete excision, which can occasionally lead to an upgrade in the resection specimen. However, alternative, less invasive treatment strategies, such as clinical follow-up, may be considered. In this study, we retrospectively analyzed diagnostic biopsies from our institution to determine the upgrade rate of each B3 lesion subgroup to breast malignancy following complete excision. Materials and Methods: All breast biopsies conducted at our institution from 1 January 2018 to 30 November 2022 and classified as B3 lesions were included in this study. The lesions were categorized into groups and subgroups based on their growth pattern and histopathological features. To determine the upgrade rate to ductal carcinoma in situ (DCIS) or invasive breast cancer (IBC) for each B3 lesion subgroup, we assessed the histological concordance between the biopsy and the resection specimen. Results: During the study period, 10,531 biopsies were performed, of which 1045 (9.93%) were classified as B3 lesions. Among these, 795 (76.08%) were subsequently resected, either through surgical procedures (98.32%) or using the Vacuum-Assisted Excision technique (1.68%). Histological examination revealed that 89 (11.19%) of the resected B3 lesions were upgraded to breast malignancy, with 59 cases (7.42%) progressing to DCIS, 22 cases (2.76%) to IBC, and 8 cases (1.01%) to borderline or malignant phyllodes tumor. The upgrade rate varied among histopathological subgroups, being lowest in complex sclerosing lesions without atypia (4.95%, 95% CI: 2.5–8.7%) and highest in intraductal papillomas with atypia (58.82%; 95% CI: 32.9–81.6%). Conclusions: Statistically significant differences were observed between B3 lesion subgroups, with a higher risk of upgrade in lesions exhibiting atypia. As our understanding of B3 lesions evolves, there is potential to implement therapeutic strategies tailored to the specific risk associated with each subgroup. This approach could allow for less invasive management options, such as clinical or radiological follow-up, thereby sparing patients from unnecessary invasive procedures when appropriate. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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14 pages, 1627 KB  
Article
Molecular Subtypes and Survival Patterns in Female Breast Cancer: Insights from a 12-Year Cohort
by Ionut Marcel Cobec, Ingolf Juhasz-Böss, Peter Seropian, Sarah Huwer, Vlad Bogdan Varzaru, Andreas Rempen and Aurica Elisabeta Moatar
Medicina 2025, 61(10), 1858; https://doi.org/10.3390/medicina61101858 - 16 Oct 2025
Viewed by 277
Abstract
Background and Objectives: Breast cancer is one of the most common cancers in women and the most common cause of cancer death. Hormone receptors, specifically the estrogen receptor (ER) and progesterone receptor (PR), as well as human epidermal growth factor receptor-2 (Her2), are [...] Read more.
Background and Objectives: Breast cancer is one of the most common cancers in women and the most common cause of cancer death. Hormone receptors, specifically the estrogen receptor (ER) and progesterone receptor (PR), as well as human epidermal growth factor receptor-2 (Her2), are tumor-specific markers used to guide breast cancer therapy. The purpose of this study is to evaluate the impact of tumor biology, including ER, PR, and Her2 expression, on survival in female breast cancer. Materials and Methods: This retrospective cohort study represents an analysis of 2016 female breast cancer cases using anonymized data. We reviewed cases of female breast cancer diagnosed from 1 January 2010 to 31 December 2021, in the Clinic of Obstetrics and Gynecology, Diakoneo Diak Klinikum Schwäbisch Hall, Germany. Data on clinical, pathology, immunohistochemistry, and follow-up characteristics were retrieved from the clinic’s database. To interpret the data, we used the software IBM SPSS Statistics 20, and, to account for multiple comparisons, we used a Bonferroni-adjusted significance level of 0.004. In the survival analysis, the Kaplan–Meier method and the log-rank test of equality of survival distributions were applied. Results: Among 2016 female breast cancer cases, 84.5% (1703/2016) were hormone receptor (HR)-positive. The 5-year overall survival was 0.873 (95% CI (0.851, 0.895); 99.6% CI (0.841, 0.905)) for HR-positive patients and 0.760 (95% CI (0.713, 0.807); 99.6% CI (0.691, 0.829)) for HR-negative patients (p < 0.001). Statistically significant differences were observed among HR+/HER2+, HR+/HER2−, HR−/HER2+, and triple-negative subtypes (p = 0.003). When comparing survival distributions based solely on HER2 expression (positive vs. negative), no statistically significant difference was observed (p = 0.29). Conclusions: Statistically significant differences in unadjusted overall survival distributions were observed among breast cancer molecular subtypes. HR-positive breast cancers demonstrated better overall survival than HR-negative cancers, while no statistically significant difference in unadjusted survival was observed between HER2-positive and HER2-negative groups. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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19 pages, 1175 KB  
Article
The Effect of the Clinical-Pathological CPS+EG Staging System on Survival Outcomes in Patients with HER2-Positive Breast Cancer Receiving Neoadjuvant Treatment: A Retrospective Study
by Seval Orman, Miray Aydoğan, Oğuzcan Kınıkoğlu, Sedat Yıldırım, Nisanur Sarıyar Busery, Hacer Şahika Yıldız, Ezgi Türkoğlu, Tuğba Kaya, Deniz Işık, Seval Ay Ersoy, Hatice Odabaş and Nedim Turan
Medicina 2025, 61(10), 1813; https://doi.org/10.3390/medicina61101813 - 9 Oct 2025
Viewed by 507
Abstract
Background and Objectives: To evaluate the prognostic value of the Clinical–Pathologic Stage–Estrogen receptor status and Grade (CPS+EG) staging system, which combines clinical staging, pathological staging, oestrogen receptor (ER) status, and tumour grade in predicting survival outcomes in patients with human epidermal growth [...] Read more.
Background and Objectives: To evaluate the prognostic value of the Clinical–Pathologic Stage–Estrogen receptor status and Grade (CPS+EG) staging system, which combines clinical staging, pathological staging, oestrogen receptor (ER) status, and tumour grade in predicting survival outcomes in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer receiving neoadjuvant therapy (NACT). Materials and Methods: A retrospective review was performed on 245 female breast cancer patients who received anti-HER2 therapy alongside NACT at the Medical Oncology Department of Kartal Dr Lütfi Kırdar City Hospital, University of Health Sciences, from April 2012 to June 2024. The CPS+EG score was calculated using the MD Anderson Cancer Centre neoadjuvant treatment response calculator. Patients were categorised into two groups based on their CPS+EG score < 3 and ≥3. The primary outcomes assessed were disease-free survival (DFS) and overall survival (OS). Kaplan–Meier and log-rank tests were utilised for time-to-event analysis; Cox regression was used for multivariate analysis. A significance level of ≤0.05 was considered. Results: The median age of the patient cohort was 51 years (range: 27–82 years). Among these patients, 183 (74.6%) had a CPS+EG score less than 3, while 62 (25.3%) exhibited a score of 3 or higher. The median follow-up duration was 37.6 months. The pathological complete response (pCR) rate across the entire cohort was 51.8%. Specifically, the pCR rate was 56.3% in the group with CPS+EG scores below 3, and 38.7% in those with scores of 3 or higher (p = 0.017). Patients with CPS+EG scores less than 3 demonstrated superior overall survival (OS), which reached statistical significance in univariate analysis. Multivariate analysis identified the CPS+EG score as an independent prognostic factor for both overall survival and disease-free survival (DFS), with hazard ratios of 0.048 (95% CI: 0.004–0.577, p = 0.017) and 0.35 (95% CI: 0.14–0.86, p = 0.023), respectively. Conclusions: The CPS+EG score is an independent and practical prognostic marker, particularly for overall survival, in patients with HER2-positive breast cancer who have received neoadjuvant therapy. Patients with a CPS+EG score < 3 have higher pCR rates and survival rates. When used in conjunction with pCR, it can improve risk categorisation and contribute to the individualisation of adjuvant strategies in the post-neoadjuvant period. Due to its ease of calculation and lack of additional costs, this score can be instrumental in clinical practice for identifying high-risk patients. Our findings support the integration of the CPS+EG score into routine clinical decision-making processes, although prospective validation studies are necessary. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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15 pages, 1341 KB  
Article
Stratifying Breast Lesion Risk Using BI-RADS: A Correlative Study of Imaging and Histopathology
by Sebastian Ciurescu, Simona Cerbu, Ciprian Nicușor Dima, Victor Buciu, Denis Mihai Șerban, Diana Gabriela Ilaș and Ioan Sas
Medicina 2025, 61(7), 1245; https://doi.org/10.3390/medicina61071245 - 10 Jul 2025
Viewed by 1188
Abstract
Background and Objectives: The accuracy of breast cancer diagnosis depends on the concordance between imaging features and pathological findings. While BI-RADS (Breast Imaging Reporting and Data System) provides standardized risk stratification, its correlation with histologic grade and immunohistochemical markers remains underexplored. This [...] Read more.
Background and Objectives: The accuracy of breast cancer diagnosis depends on the concordance between imaging features and pathological findings. While BI-RADS (Breast Imaging Reporting and Data System) provides standardized risk stratification, its correlation with histologic grade and immunohistochemical markers remains underexplored. This study assessed the diagnostic performance of BI-RADS 3, 4, and 5 classifications and their association with tumor grade and markers such as ER, PR, HER2, and Ki-67. Materials and Methods: In this prospective study, 67 women aged 33–82 years (mean 56.4) underwent both mammography and ultrasound. All lesions were biopsied using ultrasound-guided 14G core needles. Imaging characteristics (e.g., margins, echogenicity, calcifications), histopathological subtype, and immunohistochemical data were collected. Statistical methods included logistic regression, Chi-square tests, and Spearman’s correlation to assess associations between BI-RADS, histology, and immunohistochemical markers. Results: BI-RADS 5 lesions showed a 91% malignancy rate. Evaluated features included spiculated margins, pleomorphic microcalcifications, and hypoechoic masses with posterior shadowing, and were correlated with histological and immunohistochemical results. Invasive tumors typically appeared as irregular, hypoechoic masses with posterior shadowing, while mucinous carcinomas mimicked benign features. Higher BI-RADS scores correlated significantly with increased Ki-67 index (ρ = 0.76, p < 0.001). Logistic regression yielded an AUC of 0.877, with 93.8% sensitivity and 80.0% specificity. Conclusions: BI-RADS scoring effectively predicts malignancy and correlates with tumor proliferative markers. Integrating imaging, histopathology, and molecular profiling enhances diagnostic precision and supports risk-adapted clinical management in breast oncology. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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22 pages, 7258 KB  
Article
AI in 2D Mammography: Improving Breast Cancer Screening Accuracy
by Sebastian Ciurescu, Simona Cerbu, Ciprian Nicușor Dima, Florina Borozan, Raluca Pârvănescu, Diana-Gabriela Ilaș, Cosmin Cîtu, Corina Vernic and Ioan Sas
Medicina 2025, 61(5), 809; https://doi.org/10.3390/medicina61050809 - 26 Apr 2025
Cited by 1 | Viewed by 3265
Abstract
Background and Objectives: Breast cancer is a leading global health challenge, where early detection is essential for improving survival outcomes. Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast [...] Read more.
Background and Objectives: Breast cancer is a leading global health challenge, where early detection is essential for improving survival outcomes. Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast density and inter-reader variability. Recent advances in artificial intelligence (AI) have shown promise in enhancing radiological interpretation. This study aimed to assess the utility of AI in improving lesion detection and classification in 2D mammography. Materials and Methods: A retrospective analysis was performed on a dataset of 578 mammographic images obtained from a single radiology center. The dataset consisted of 36% pathologic and 64% normal cases, and was partitioned into training (403 images), validation (87 images), and test (88 images) sets. Image preprocessing involved grayscale conversion, contrast-limited adaptive histogram equalization (CLAHE), noise reduction, and sharpening. A convolutional neural network (CNN) model was developed using transfer learning with ResNet50. Model performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUC-ROC) curve. Results: The AI model achieved an overall classification accuracy of 88.5% and an AUC-ROC of 0.93, demonstrating strong discriminative capability between normal and pathologic cases. Notably, the model exhibited a high specificity of 92.7%, contributing to a reduction in false positives and improved screening efficiency. Conclusions: AI-assisted 2D mammography holds potential to enhance breast cancer detection by improving lesion classification and reducing false-positive findings. Although the model achieved high specificity, further optimization is required to minimize false negatives. Future efforts should aim to improve model sensitivity, incorporate multimodal imaging techniques, and validate results across larger, multicenter prospective cohorts to ensure effective integration into clinical radiology workflows. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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16 pages, 22961 KB  
Article
Role of Progesterone Receptor Level in Predicting Axillary Lymph Node Metastasis in Clinical T1-T2N0 Luminal Type Breast Cancer
by Mihriban Erdogan, Canan Kelten Talu, Zeliha Guzeloz, Gonul Demir, Ferhat Eyiler, Seval Akay, Ezgi Yilmaz and Olcun Umit Unal
Medicina 2025, 61(4), 710; https://doi.org/10.3390/medicina61040710 - 12 Apr 2025
Viewed by 934
Abstract
Background and Objectives: Axillary lymph node metastasis and the number of metastatic lymph nodes are important prognostic factors which are directly related to overall survival in women with breast cancer. Several factors have been identified to predict the likelihood of axillary lymph [...] Read more.
Background and Objectives: Axillary lymph node metastasis and the number of metastatic lymph nodes are important prognostic factors which are directly related to overall survival in women with breast cancer. Several factors have been identified to predict the likelihood of axillary lymph node metastasis in early-stage breast cancer. High PR expression is often more prevalent in the luminal A subgroup, which is associated with a better prognosis. The aim of this study was to determine the relationship between the percentage of PR expression and the likelihood of axillary metastasis in Her-2-negative, clinical T1-T2N0 luminal type breast cancer. Materials and Methods: A hundred and ninety-nine cases with luminal type, Her-2-negative, clinically and radiologically axilla-negative T1-T2 breast cancer who received radiotherapy were evaluated retrospectively. The pathological specimens were assessed by an experienced pathologist. Results: The statistical evaluation showed that tumor diameter greater than 2 cm, (p = 0.003), presence of lymphovascular invasion (p = 0.001), and PR expression level below 80% (p = 0.037) were identified as significant predictors of lymph node positivity in breast cancer patients. Conclusions: Percentage of progesterone receptor expression along with other molecular biological markers and clinicopathological parameters should be evaluated altogether when predicting axillary metastasis risk before surgery. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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Review

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19 pages, 495 KB  
Review
Redefining Breast Cancer Care by Harnessing Computational Drug Repositioning
by Elena-Daniela Jurj, Daiana Colibășanu, Sabina-Oana Vasii, Liana Suciu, Cristina Adriana Dehelean and Lucreția Udrescu
Medicina 2025, 61(9), 1640; https://doi.org/10.3390/medicina61091640 - 10 Sep 2025
Cited by 1 | Viewed by 872
Abstract
Breast cancer faces significant therapeutic challenges, particularly for triple-negative breast cancer (TNBC), due to limited targeted therapies and drug resistance. Drug repositioning leverages existing safety and pharmacokinetic data to expedite the identification of new indications with cost-effective benefits compared to de novo drug [...] Read more.
Breast cancer faces significant therapeutic challenges, particularly for triple-negative breast cancer (TNBC), due to limited targeted therapies and drug resistance. Drug repositioning leverages existing safety and pharmacokinetic data to expedite the identification of new indications with cost-effective benefits compared to de novo drug discovery. In this critical narrative review, we examine recent advances in computational repositioning strategies for breast cancer, focusing on network-based methods, computer-aided drug design, artificial intelligence and machine learning, transcriptomic signature matching, and multi-omics integration. We highlight key case studies that have progressed to preclinical validation or clinical evaluation. We assess comparative performance metrics, experimental validation outcomes, and real-world success rates. We also present critical methodological challenges, including data heterogeneity, bias in real-world data, and the need for study reproducibility. Our review emphasizes the importance of window-of-opportunity trials and the need for standardized data sharing and reproducible pipelines. These insights highlight the groundbreaking potential of in silico repositioning in addressing unmet needs in breast cancer therapy. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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16 pages, 1767 KB  
Review
Current Endocrine Therapy in Hormone-Receptor-Positive Breast Cancer: From Tumor Biology to the Rationale for Therapeutic Tunning
by Oana Maria Burciu, Adrian-Grigore Merce, Simona Cerbu, Aida Iancu, Tudor-Alexandru Popoiu, Ionut Marcel Cobec, Ioan Sas and Gabriel Mihail Dimofte
Medicina 2025, 61(7), 1280; https://doi.org/10.3390/medicina61071280 - 16 Jul 2025
Viewed by 1491
Abstract
Background and Objectives: The objective of this review is to evaluate the current evidence regarding hormone treatments for both premenopausal and postmenopausal women with early-stage hormone receptor (HR) positive breast cancer. Materials and Methods: An in-depth exploration of the existing literature was [...] Read more.
Background and Objectives: The objective of this review is to evaluate the current evidence regarding hormone treatments for both premenopausal and postmenopausal women with early-stage hormone receptor (HR) positive breast cancer. Materials and Methods: An in-depth exploration of the existing literature was conducted, with landmark clinical trials such as TEXT, SOFT, ATLAS, and aTTom serving as primary references. Results: Through an extensive review of the literature, our findings indicate that for premenopausal women with HR-positive, HER2-negative BC with a low risk of recurrence, standard 5-year monotherapy with tamoxifen represents the optimal therapeutic management, given its favorable clinical outcomes and lower associated toxicity. In contrast, for premenopausal women with an intermediate to high risk of recurrence with the same tumor characteristics, the most effective approach stated in the literature is a combination of ovarian suppression therapy (chemical/surgical) and an aromatase inhibitor/selective estrogen receptor modulator (tamoxifen), with a possible extension of the standard therapeutic period. In postmenopausal patients with HR-positive, HER2-negative breast cancer with a low recurrence risk, the first line of treatment is usually a standard 5-year period of treatment with aromatase inhibitors (AIs)(letrozole, anastrozole, or exemestane). On the other hand, in postmenopausal women with an intermediate to high risk, combination therapy might be needed, as well as an extension of the standard therapeutic time. Conclusions: Treatment consensus depends on pre- vs. postmenopausal status and recurrence risk. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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Other

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16 pages, 909 KB  
Systematic Review
Systematic Review and Meta-Analysis of AI-Assisted Mammography and the Systemic Immune-Inflammation Index in Breast Cancer: Diagnostic and Prognostic Perspectives
by Sebastian Ciurescu, Maria Ciupici-Cladovan, Victor Bogdan Buciu, Diana Gabriela Ilaș, Cosmin Cîtu and Ioan Sas
Medicina 2025, 61(7), 1170; https://doi.org/10.3390/medicina61071170 - 27 Jun 2025
Cited by 1 | Viewed by 2125
Abstract
Background and Objectives: Breast cancer remains a significant global health burden, demanding continuous innovation in diagnostic and prognostic tools. This meta-analysis and systematic review aims to synthesize evidence from 2015 to 2025 regarding the diagnostic utility of artificial intelligence (AI) in mammography [...] Read more.
Background and Objectives: Breast cancer remains a significant global health burden, demanding continuous innovation in diagnostic and prognostic tools. This meta-analysis and systematic review aims to synthesize evidence from 2015 to 2025 regarding the diagnostic utility of artificial intelligence (AI) in mammography and the prognostic value of the Systemic Immune-Inflammation Index (SII) in breast cancer patients. Materials and Methods: A systematic literature search was conducted in PubMed, Google Scholar, EMBASE, Web of Science, and Scopus. Studies evaluating AI performance in mammographic breast cancer detection and those assessing the prognostic significance of SII (based on routine hematologic parameters) were included. The risk of bias was assessed using QUADAS-2 and the Newcastle–Ottawa Scale. Meta-analyses were conducted using bivariate and random-effects models, with subgroup analyses by clinical and methodological variables. Results: Twelve studies were included, five assessing AI and seven assessing SII. AI demonstrated high diagnostic accuracy, frequently matching or surpassing that of human radiologists, with AUCs of up to 0.93 and notable reductions in radiologist reading times (17–91%). Particularly in dense breast tissue, AI improved detection rates and workflow efficiency. SII was significantly associated with poorer outcomes, including reduced overall survival (HR ~1.97) and disease-free survival (HR ~2.07). However, variability in optimal cut-off values for SII limits its immediate clinical standardization. Conclusions: AI enhances diagnostic precision and operational efficiency in mammographic screening, while SII offers a cost-effective prognostic biomarker for systemic inflammation in breast cancer. Their integration holds promise for more personalized care. Nevertheless, challenges persist regarding prospective validation, standardization, and equitable access, which must be addressed through future translational research. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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