{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T12:29:57Z","timestamp":1786364997835,"version":"build-2736575974"},"reference-count":32,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Objectives: We evaluated the performance of mammography-based radiomics in prediction of clinical nodal status, clinical tumor stage, clinical disease stage, status of the PIK3CA mutation and concordance with the radiologist-assessed BI-RADS category. Mammography is often the first imaging modality performed in the screening or diagnostic workup of breast cancer. Materials and Methods: In this single-center retrospective study, we included 102 histopathologically confirmed cases of breast cancer from 100 patients. The tumor region and whole-breast parenchyma were contoured on 368 craniocaudal and mediolateral-oblique mammograms. Cases were temporally split into training and test sets by histopathological diagnosis date (training: 70%, held-out test: 30%). Six linear models were tuned by 5-fold x 3-repeat stratified cross-validation. The winning model per endpoint was applied once to the test set. We report AUCs with 95% confidence intervals, permutation p-values and Benjamini\u2013Hochberg false discovery rate (BH-FDR) correction across five endpoints. Results: Three of the studied endpoints reached significance after BH-FDR correction: clinical nodal status (cN0 vs. cN-positive; AUC: 0.726 (95% CI: 0.501\u20130.886)), clinical tumor stage (cT1-2 vs. cT3-4; AUC: 0.792 (95% CI: 0.575\u20130.929)) and overall disease stage (I\u2013II vs. III\u2013IV; AUC: 0.778 (95% CI: 0.572\u20130.917)). Mammography-based radiomics failed to predict the presence of PIK3CA mutation and concordance with radiologist-assessed BI-RADS category. Conclusions: Mammography-based radiomics has shown a hypothesis-generating discriminative value for differentiation between cN-negative and cN-positive disease, early from advanced clinical tumor stage, and early from advanced overall disease stage in breast cancer. Mammography-based radiomics did not predict PIK3CA status and did not discriminate between radiologist-assessed BI-RADS 4 and 5 groups.<\/jats:p>","DOI":"10.3390\/cancers18162566","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T11:38:09Z","timestamp":1786361889000},"page":"2566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mammography-Based Radiomics for Prediction of Nodal Status and Disease Burden in Breast Cancer: A Temporally Validated Study"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7835-523X","authenticated-orcid":false,"given":"Grzegorz","family":"Chmielewski","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Holy Cross Cancer Center, 25-734 Kielce, Poland"},{"name":"Collegium Medicum, Jan Kochanowski University, 25-317 Kielce, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2543-8370","authenticated-orcid":false,"given":"Rafa\u0142","family":"Stando","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Holy Cross Cancer Center, 25-734 Kielce, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hubert S.","family":"Gabry\u015b","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maksym","family":"Fritsak","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie","family":"Tanadini-Lang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Guckenberger","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanis\u0142aw","family":"G\u00f3\u017ad\u017a","sequence":"additional","affiliation":[{"name":"Collegium Medicum, Jan Kochanowski University, 25-317 Kielce, Poland"},{"name":"Department of Clinical Oncology, Holy Cross Cancer Center, 25-734 Kielce, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"ref_1","unstructured":"(2026, May 03). Data Explorer|ECIS\u2014European Cancer Information System [Internet]. Available online: https:\/\/ecis.jrc.ec.europa.eu\/data-explorer#\/estimates\/estimated-incidence-mortality-by-country-summary?indicator=MO&sex=0&yearFrom=2024&yearTo=2024&ageFrom=0&ageTo=85%2B&cancerEntity=29&country=EU27."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e089428","DOI":"10.1136\/bmjopen-2024-089428","article-title":"National yearly cost of breast cancer screening in the USA and projected cost of advocated guidelines: A simulation study with life table modelling","volume":"15","author":"Badal","year":"2025","journal-title":"BMJ Open"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1148\/radiol.2016161174","article-title":"National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium","volume":"283","author":"Lehman","year":"2017","journal-title":"Radiology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6121","DOI":"10.1007\/s00330-024-10638-2","article-title":"Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography","volume":"34","author":"Wang","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2413702","DOI":"10.1002\/advs.202413702","article-title":"Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer","volume":"12","author":"Huang","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.1016\/j.acra.2023.12.012","article-title":"Prediction of Receptor Status in Radiomics: Recent Advances in Breast Cancer Research","volume":"31","author":"Lin","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xie, H., Tan, T., Li, Q., and Li, T. (2025). Revolutionizing HER-2 assessment: Multidimensional radiomics in breast cancer diagnosis. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-13549-7"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_11","unstructured":"Jolesz, F.A. (2014). 3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support. Intraoperative Imaging and Image-Guided Therapy, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R Stat. Soc. Ser. B Methodol."},{"key":"ref_13","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn Res."},{"key":"ref_14","unstructured":"Seabold, S., and Perktold, J. (July, January 20). Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.acra.2023.10.010","article-title":"MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis","volume":"31","author":"Tabnak","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","article-title":"Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI","volume":"19","author":"Braman","year":"2017","journal-title":"Breast Cancer Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13058-019-1187-z","article-title":"Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: Initial results","volume":"21","author":"Leithner","year":"2019","journal-title":"Breast Cancer Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1080\/14737140.2022.2064277","article-title":"Addressing the problem of overtreatment in breast cancer","volume":"22","author":"Pak","year":"2022","journal-title":"Expert Rev. Anticancer Ther."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Flemban, A.F. (2023). Overdiagnosis Due to Screening Mammography for Breast Cancer among Women Aged 40 Years and Over: A Systematic Review and Meta-Analysis. J. Pers. Med., 13.","DOI":"10.3390\/jpm13030523"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1016\/j.acra.2021.09.025","article-title":"Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs","volume":"29","author":"Siviengphanom","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1007\/s10278-022-00753-1","article-title":"A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images","volume":"36","author":"Letchumanan","year":"2023","journal-title":"J. Digit. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35311","DOI":"10.1038\/s41598-025-19307-w","article-title":"Breast microcalcifications as indicators of bone metastasis risk in early-stage breast cancer","volume":"15","author":"Giorello","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.molonc.2015.02.001","article-title":"Disorganised stroma determined on pre-treatment breast cancer biopsies is associated with poor response to neoadjuvant chemotherapy: Results from the NEOZOTAC trial","volume":"9","author":"Dekker","year":"2015","journal-title":"Mol. Oncol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20191019","DOI":"10.1259\/bjr.20191019","article-title":"Mammography-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in breast cancer","volume":"93","author":"Tan","year":"2020","journal-title":"Br. J. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4429","DOI":"10.1038\/s41598-019-40831-z","article-title":"Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method","volume":"9","author":"Yang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1186\/s40001-025-03435-0","article-title":"Predicting PIK3CA mutation in breast cancer with machine learning based multimodal image radiomics","volume":"30","author":"Yao","year":"2025","journal-title":"Eur. J. Med. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.annonc.2020.11.011","article-title":"Alpelisib plus fulvestrant for PIK3CA-mutated, hormone receptor-positive, human epidermal growth factor receptor-2\u2013negative advanced breast cancer: Final overall survival results from SOLAR-1","volume":"32","author":"Ciruelos","year":"2021","journal-title":"Ann. Oncol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Suzuki, Y., Hanaoka, S., Tanabe, M., Yoshikawa, T., and Seto, Y. (2023). Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images. J. Pers. Med., 13.","DOI":"10.3390\/jpm13111528"},{"key":"ref_31","first-page":"325","article-title":"Artificial Intelligence in Cancer Management: Promise, Pragmatism, and Preparedness","volume":"11","author":"Robatjazi","year":"2026","journal-title":"Asian Pac. J. Cancer Care"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Usman Sami, M., Saeed, M., and Nazeem, S. (2026). Diagnostic Accuracy of AI-assisted Mammography in Breast Cancer Detection: A Retrospective Study from a South Asian Population. Asian Pac. J. Cancer Nurs.","DOI":"10.31557\/APJCN.2561.20260531"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/16\/2566\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T11:47:11Z","timestamp":1786362431000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/16\/2566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,10]]},"references-count":32,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["cancers18162566"],"URL":"https:\/\/doi.org\/10.3390\/cancers18162566","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,10]]}}}