Breast Cancer Research最新文献

{"title":"Hope for OTHERS (Our Tissue Helping Enhance Research & Science): research results from the University of Pittsburgh rapid autopsy program for breast cancer.","authors":"Alexander Chih-Chieh Chang, Marija Balic, Tanner Bartholow, Rohit Bhargava, Daniel D Brown, Lauren Brown, Adam Brufsky, Ye Cao, Neil Carleton, Amanda M Clark, Morgan Cody, Kai Ding, Christopher Deible, Ashuvinee Elangovan, Julia Foldi, Daniel Geisler, Christine Hodgdon, Naomi Howard, Zheqi Li, Jie Bin Liu, Oscar Lopez-Nunez, Dixcy Jaba Sheeba John Mary, Olivia McGinn, Lori Miller, Kanako Mori, Geoffrey Pecar, Nolan Priedigkeit, Shannon Puhalla, Margaret Q Rosenzweig, Partha Roy, Laura Savariau, Stephanie Walker, Hunter Waltermire, Abdalla M Wedn, Alan Wells, Megan E Yates, Jennifer Xavier, Adrian V Lee, Steffi Oesterreich","doi":"10.1186/s13058-025-02014-9","DOIUrl":"10.1186/s13058-025-02014-9","url":null,"abstract":"<p><p>Breast cancer affects 1/8 of women throughout their lifetimes, with over 90% of cancer deaths being caused by metastasis. However, metastasis poses unique challenges to research, as complex changes in the microenvironment in different metastatic sites and difficulty obtaining tissue for study hinder the ability to examine in depth the changes that occur during metastasis. Rapid autopsy programs thus fill a unique need in advancing metastasis research. Here, we describe our protocol and processes for establishing and improving the US-based Hope for OTHERS (Our Tissue Helping Enhance Research and Science) program for organ donation in metastatic breast cancer. As of August 2024, we consented 114 patients and performed 37 autopsies, from which we collected 551 unique metastatic frozen tumor samples, 1244 FFPE blocks, 90 longitudinal liquid biopsy samples and developed 14 patient-derived organoid and 8 patient-derived xenograft models. We report in-depth clinical and histopathological information and discuss extensive new research and novel findings in patient outcomes, metastatic phylogeny, and factors in successful living model development. Our results reveal key logistical and protocol improvements that are uniquely beneficial to certain programs based on identifiable features, such as working closely with patient advocates, methods to rescue RNA quality in cases where tissue quality may degrade due to time delays, as well as guidelines and future expansions of our program.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"111"},"PeriodicalIF":7.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meeting Abstracts from the British Society of Breast Radiology Annual Scientific Meeting 2024.","authors":"","doi":"10.1186/s13058-025-02023-8","DOIUrl":"10.1186/s13058-025-02023-8","url":null,"abstract":"","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 Suppl 1","pages":"98"},"PeriodicalIF":7.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.","authors":"Xue Chao, Yu Wu, Xi Cai, Jiehua He, Chengyou Zheng, Mei Li, Rongzhen Luo, Lijuan Song, Xiaoqin Li, Wentai Feng, Shuoyu Xu, Peng Sun","doi":"10.1186/s13058-025-02064-z","DOIUrl":"10.1186/s13058-025-02064-z","url":null,"abstract":"<p><strong>Background: </strong>Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, highlight the need for improved diagnostic tools. This study aims to develop and validate a deep-learning model that transforms cryosectioned images into formalin-fixed paraffin-embedded (FFPE) images to enhance diagnostic performance in breast lesions.</p><p><strong>Methods: </strong>We developed an unpaired image-to-image translation model (AI-FFPE) using the TCGA-BRCA dataset to convert FS images into FFPE-like images. The model employs a modified generative adversarial network (GAN) enhanced with an attention mechanism to correct artifacts and a self-regularization constraint to preserve clinically significant features. For validation, 132 FS whole slide images (WSIs) of breast lesions were collected from three cohorts (SYSUCC, GSPCH, and TCGA). These FS-WSIs were transformed into AI-FFPE-WSIs and independently evaluated by six pathologists for image quality, diagnostic concordance, and confidence in lesion properties and final diagnoses. Diagnostic performance was assessed using a diagnostic score (DS), calculated by multiplying the accuracy index by the confidence level. The dataset included 132 reference diagnoses and 1,584 pathologist reads.</p><p><strong>Results: </strong>The AI-FFPE group showed a significant improvement in image quality compared to the FS group (p < 0.001). Concordance rates for lesion properties (79.9% vs. 79.9%) and final diagnoses (82.7% vs. 82.6%) were similar between two groups. In concordant cases, the AI-FFPE group demonstrated significantly higher diagnostic confidence than the FS group, with more diagnoses definitively categorized based on lesion properties (54.3% vs. 35.4%, p < 0.001) and final diagnoses (48.3% vs. 33.3%, p < 0.001). Paired t-tests revealed that the diagnostic scores in the AI-FFPE group were significantly higher than in the FS group (overall DS, 13.9 ± 6.6 vs. 12.0 ± 6.6, p < 0.001; DS for lesion property, 6.8 ± 3.8 vs. 5.8 ± 3.7, p < 0.001; DS for final diagnosis, 7.1 ± 3.2 vs. 6.2 ± 3.2, p < 0.001). Logistic regression showed that poorer image quality, atypical ductal hyperplasia/ ductal carcinoma in situ cases, and less experienced pathologists were associated with decreased diagnostic accuracy.</p><p><strong>Conclusions: </strong>The AI-FFPE model improved perceived image quality and diagnostic confidence among pathologists in breast lesion evaluations. While diagnostic concordance remained comparable, the enhanced interpretability of AI-FFPE images may support more confident intraoperative decision-making.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"110"},"PeriodicalIF":7.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Axillary adipose tissue-derived lymphatic endothelial cells exhibit distinct transcriptomic signatures reflecting lymphatic invasion status in breast cancer.","authors":"Asumi Iesato, Jun Suzuka, Kazutaka Otsuji, Tomo Osako, Nami Yamashita, Yuka Inoue, Tetsuyo Maeda, Natsue Uehiro, Kazuyo Yoshida, Yoko Takahashi, Kohei Kumegawa, Sumito Saeki, Liying Yang, Ai Tsuchiya, Kana Sakiyama, Miwa Tanaka, Takehiko Sakai, Shinji Ohno, Tetsuo Noda, Takayuki Ueno, Reo Maruyama","doi":"10.1186/s13058-025-02067-w","DOIUrl":"10.1186/s13058-025-02067-w","url":null,"abstract":"<p><strong>Background: </strong>Lymphatics provide a route for breast cancer cells to metastasize. Lymphatic endothelial cells (LECs), which form the structure of lymphatic vessels, play a key role in this process. Although LECs are pivotal in cancer progression, studies often rely on commercially available cell lines that may not accurately reflect the tumor microenvironment. Therefore, there is a pressing need to directly study patient-derived LECs to better understand their role in breast cancer.</p><p><strong>Methods: </strong>This study developed a method to isolate and characterize LECs directly from human breast-to-axilla adipose tissue. We used magnetic cell separation to remove CD45 + leukocytes and fluorescence-activated cell sorting to isolate cells expressing CD31 and podoplanin. Isolated cells were cultured under conditions promoting endothelial cell growth and were characterized through various assays assessing proliferation, tube formation, and gene expression patterns.</p><p><strong>Results: </strong>The sorted CD31 + /PDPN + /CD45 - cell populations exhibited marked increases in proliferation upon VEGF-C stimulation and formed tubule structures on BME-coated dishes, confirming their LEC properties. Notably, isolated LECs showed distinct gene expression patterns depending on the presence of lymph node metastasis and lymphatic invasion.</p><p><strong>Conclusions: </strong>The ability to isolate and characterize patient-derived LECs from mammary adipose tissue offers new insights into the cellular mechanisms underlying breast cancer metastasis. Significant gene expression variability related to disease state highlights the potential of these cells as biomarkers and therapeutic targets. This study emphasizes the importance of using patient-derived cells to accurately assess the tumor microenvironment, potentially leading to more personalized therapeutic approaches.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"109"},"PeriodicalIF":7.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12172209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery and validation of cell-free DNA methylation markers for specific diagnosis, differentiation from benign tumors, and prognosis of breast cancer.","authors":"Lijing Gao, Lei Zhang, Jinyin Liu, Liwan Wang, Qingzhen Fu, Siyu Liu, Yanbing Li, Chao Qu, Ning Zhao, Shiheng Tan, Fang Fang, Tian Tian, Shi Jiang, Junxiao Wu, Yan Dong, Yajie Gong, Yukun Cao, Fan Wang, Xianyu Zhang, Da Pang, Yashuang Zhao","doi":"10.1186/s13058-025-02066-x","DOIUrl":"10.1186/s13058-025-02066-x","url":null,"abstract":"<p><strong>Background: </strong>Plasma cell-free DNA (cfDNA) methylation is emerging as a non-invasive marker for various cancers. We aimed to identify specific methylation markers for diagnosis, differentiation from benign tumors, and prognosis of breast cancer (BC), which are essential for clinical decision-making yet seldom examined together.</p><p><strong>Methods: </strong>BC-specific methylation markers were identified using an in-house 850K dataset combined with large-scale publicly available 450 or 850K datasets. Multiplex digital droplet PCR (mddPCR) assays were developed to detect methylation in cfDNA from 201 BC patients, 83 healthy donors, and 71 individuals harboring benign tumors. Diagnostic and prognostic performance were evaluated using logistic and Cox regression models, respectively. The basic mechanism of a selected gene was explored through in vitro experiments.</p><p><strong>Results: </strong>We identified 21 BC-specific methylated CpG sites that distinguished BC from tumor-adjacent tissues with high diagnostic accuracy. In the cfDNA cohort, three mddPCR assays targeting eight markers achieved an area under the curve (AUC) of 0.856 (95% CI = 0.814-0.898) for distinguishing BC from healthy controls, and 0.742 (95% CI = 0.684-0.801) for differentiating BC from benign tumors. Notably, combining these methylation markers with mammography and ultrasound improved diagnostic performance, resulting in an AUC of 0.898 (95% CI = 0.858-0.938) for differentiating BC from benign tumors. In the TCGA-BC dataset, prognostic model based on six sites was associated with poor overall survival prognosis (hazard ratio = 2.826, 95%CI: 1.841-4.338, p < 0.0001). In vitro experiments elucidated that FAM126A overexpression regulates BC cells malignant phenotypes.</p><p><strong>Conclusions: </strong>Our study demonstrated potential values of methylation-based markers in the detection and prognosis of BC.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"108"},"PeriodicalIF":7.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12172351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-dimensional analysis of plasma-derived extracellular vesicles to determine the HER2 status in breast cancer patients.","authors":"Alexis Wilhelm, Charlotte Flynn, Evelyn Hammer, Johannes Roessler, Bernhard Haller, Rudolf Napieralski, Moritz Leuthner, Sanja Tosheska, Kèvin Knoops, Anjusha Mathew, Giuliano Ciarimboli, Jan Kranich, Lavinia Flaskamp, Siobhan King, Heidrun Gevensleben, Quirin Emslander, Anna Pastucha, Mathias Reisbeck, Lukas Rief, Holger Bronger, Tobias Dreyer, Andreas R Bausch, Andreas Pichlmair, Thomas Brocker, Reinhard Zeidler, Wolfgang Hammerschmidt, Melanie Piedavent-Salomom, Carmen López-Iglesias, Gabrielle Schricker, Oliver Haydn, Marion Kiechle, Sabine Grill, Ron Heeren, Percy A Knolle, Olaf Wilhelm, Bastian Höchst","doi":"10.1186/s13058-025-02056-z","DOIUrl":"10.1186/s13058-025-02056-z","url":null,"abstract":"<p><p>Breast cancer, one of the most common cancers in women, is classified by the expression of hormone receptors and the growth factor receptor HER2, which is important for personalised tumour treatment with HER2-targeted therapies. Tumour biopsies are required for histopathological diagnosis of HER2 expression by breast cancer cells but are subject to sampling error. In this study, we present a method for identifying and analysing cancer-derived EVs from plasma for the detection of HER2 expression in breast cancer without the need for additional processing steps. We detected nano-sized particles through an optimised flow cytometry approach that allows for the identification of HER2-expressing EVs and quantification of their HER2 expression levels. In a clinical study of 115 breast cancer patients, this optimised flow cytometric analysis detected a range of 1.3 to 50 × 10³ HER2⁺EVs per µl of plasma. The number of HER2⁺EVs did not correlate directly with tumour size, grade, or metastasis. However, computational integration of data from the quantification of HER2^pos EVs per µl/plasma and their HER2 expression levels on a single EV basis allowed for the reliable identification of HER2 expression levels in tumours. Our results reveal the potential for analysing cancer-derived EVs from plasma for the diagnosis and personalised therapy in breast cancer patients.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"107"},"PeriodicalIF":7.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12168403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of HER2-low with clinicopathological features in patients with early invasive lobular breast cancer: an international multicentric study.","authors":"Karen Van Baelen, Ha-Linh Nguyen, François Richard, Gitte Zels, Maria Margarete Karsten, Guilherme Nader-Marta, Peter Vermeulen, Luc Dirix, Adam David Dordevic, Evandro de Azambuja, Denis Larsimont, Marion Maetens, Elia Biganzoli, Hans Wildiers, Ann Smeets, Ines Nevelsteen, Patrick Neven, Giuseppe Floris, Christine Desmedt","doi":"10.1186/s13058-025-02058-x","DOIUrl":"10.1186/s13058-025-02058-x","url":null,"abstract":"<p><strong>Purpose: </strong>The antibody-drug conjugate trastuzumab deruxtecan has proven to be not only efficient in patients with HER2+ breast cancers (BC), but also in those patients with so-called HER2-low BC. HER2-low tumors are well described in the general BC population, but not in patients with invasive lobular carcinoma (ILC). Here, we aimed at analyzing the association of HER2-low with clinicopathological features and survival outcomes in patients with early-stage pure ILC.</p><p><strong>Methods: </strong>A multicentric retrospective cohort of patients diagnosed with stage I-III estrogen receptor positive (ER+) HER2 negative (HER2-) ILC between 01/01/2000 and 12/31/2020 was assembled. HER2- disease was categorized further by immunohistochemical (IHC) score into HER2 0, HER2 1+ and HER2 2+ following time appropriate ASCO/CAP guidelines from 2007 onward and by local guidelines prior to 2007. The association of HER2-low (HER2 1+ and 2+) with clinicopathological variables was assessed using multinomial logistic regression. Survival analyses were performed to evaluate the association of HER2-low with disease-free (DFS), distant recurrence-free (DRFS) and overall survival (OS).</p><p><strong>Results: </strong>The data of 2098 patients with ER+ HER2- ILC was collected of which 1103 (52.6%) had a HER2-low tumor. Of these 716 (34.1%) had an IHC score of HER2 1+ and 387 (18.4%) of HER2 2+. In multivariable analysis, both tumor size of ≥ 2cm (OR: 1.37; 95%CI 1.01 - 1.87; p-value 0.042) and multifocality (OR: 1.55; 95%CI 1.11 - 2.15; p-value 0.009) were associated with HER2-low. HER2-low was associated with worse DFS (HR: 1.32; 95%CI 1.06 - 1.66; p-value 0.015) and OS (HR: 1.42; 95%CI 1.12 - 1.81; p-value 0.004) as compared to HER2 0. No association of HER2-low with DRFS was observed.</p><p><strong>Conclusions: </strong>HER2-low is present in more than half of the patients with early ER+ HER2- pure ILCs and is associated with larger tumor size and multifocality. HER2-low is associated with a worse DFS and OS as compared to HER2 0.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"106"},"PeriodicalIF":7.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.","authors":"Ziting Xu, Shengzhou Zhong, Yang Gao, Jiekun Huo, Weimin Xu, Weijun Huang, Xiaomei Huang, Chifa Zhang, Jianqiao Zhou, Qing Dan, Lian Li, Zhouyue Jiang, Ting Lang, Shuying Xu, Jiayin Lu, Ge Wen, Yu Zhang, Yingjia Li","doi":"10.1186/s13058-025-02060-3","DOIUrl":"10.1186/s13058-025-02060-3","url":null,"abstract":"","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"105"},"PeriodicalIF":7.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12164133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable device for axillary lymph node screening in breast cancer based on infrared thermography and artificial intelligence.","authors":"Xiaoying Zhong, Jinqiu Deng, Ping Lu, Zhichao Zuo, Yu Zhao, Yidong Zhou, Xuefei Wang","doi":"10.1186/s13058-025-02027-4","DOIUrl":"10.1186/s13058-025-02027-4","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BC) is the most prevalent cancer among women worldwide, and patients with metastasis to axillary lymph nodes (ALN) experience significantly lower survival rates. Current imaging-based screening methods often suffer from low sensitivity and limited accessibility for detecting ALN metastasis in breast cancer patients. In this study, we present an AI-based infrared thermography system for ALN metastasis detection to improve diagnostic accessibility and reduce intervention-related morbidity.</p><p><strong>Methods: </strong>In this study, we curated an internal and external cohort for developing and accessing the deep learning model-based infrared thermography system. The internal cohort included 460 inpatient participants from Peking Union Medical College Hospital, randomly divided into a training set (70%) for model development and a hold-out internal validation set (30%) for initially model evaluation. The external cohort, consisting of 80 patients from both outpatient and inpatient departments recruited from Longfu Hospital, served for independent validation of the developed screening tool.</p><p><strong>Results: </strong>The developed model AI-IRT for axillary lymph node (ALN) metastasis detection exhibited high diagnostic performance, achieving an Area Under the Curve (AUC) of 0.9424 and an accuracy of 0.8478 in the internal validation set, with a sensitivity of 0.8958 and specificity of 0.8222. In a tertiary classification scenario, the model produced an AUC of 0.8936, with corresponding accuracy, sensitivity, and specificity values of 0.7246, 0.7246, and 0.7852, respectively. In the external validation set, the AI-IRT system achieved an AUC of 0.881 and an accuracy of 0.875, with a sensitivity of 0.892 and specificity of 0.861. For the tertiary classification, the model attained an AUC of 0.771 and an accuracy of 0.613, with both sensitivity and specificity at 0.613 and 0.695, respectively.</p><p><strong>Conclusion: </strong>Evaluated on both curated internal and external cohorts, the proposed AI-IRT demonstrated strong performance across multiple centers, highlighting its potential to enhance pre-operative and intra-operative decision-making in the treatment of breast cancer patients.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"104"},"PeriodicalIF":7.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12164098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of adjuvant bone modifying agent use in patients with early-stage breast cancer in the United States.","authors":"Nicole Odzer, Rachel Jaber Chehayeb, Sarah E Schellhorn, Maryam Lustberg, Cary P Gross, Do Lee, Julia Foldi","doi":"10.1186/s13058-025-02062-1","DOIUrl":"10.1186/s13058-025-02062-1","url":null,"abstract":"<p><strong>Purpose: </strong>Based on improved survival, the 2017 ASCO and Cancer Care Ontario clinical guidelines (ACGD) recommended consideration of adjuvant bisphosphonates for postmenopausal women with early-stage breast cancer (EBC). However, small survey-based studies suggest inconsistent prescribing. This study evaluated receipt of adjuvant bone modifying agents (BMAs) in the United States before and after publication of the 2017 ACGD.</p><p><strong>Methods: </strong>This nationwide retrospective cohort study used a deidentified electronic health record-derived database to identify patients diagnosed with stage I-III EBC treated at health care practices from 2012 to 2019. We defined adjuvant BMA (bisphosphonates or denosumab) use as first dose received within 24 months of EBC diagnosis. We used Chi-squared and multivariable logistic regression analyses to compare the proportion of patients receiving adjuvant BMAs pre- and post-ACGD and identify factors associated with receipt of any BMA as well as bisphosphonates alone.</p><p><strong>Results: </strong>Our cohort included 11,470 patients. Most patients were 50 years of age or older (82%), and had stage I (57%), node-negative (70%) and estrogen receptor (ER)-positive (76%) breast cancer. Patients diagnosed post-ACGD (2017-19) were more likely to receive adjuvant BMAs (9%) than patients diagnosed in earlier years (7.4%; odds ratio [OR] 1.23; 95% confidence interval (CI) 1.08-1.42; p = 0.002). Post-menopausal status, age ≥ 50, receipt of adjuvant chemotherapy and endocrine therapy, and coexisting bone loss diagnoses were significantly associated with increased receipt of adjuvant BMAs. Among BMA recipients, 65.8% received denosumab only, 32.6% received bisphosphonates only, and 1.4% received both.</p><p><strong>Conclusions: </strong>Even after release of the ACGD guidelines, adjuvant BMA prescribing was low, and the majority of patients who received BMA did not receive bisphosphonates.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"102"},"PeriodicalIF":7.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12160112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}