{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:43:37Z","timestamp":1790959417959,"version":"4.1.0"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health\u2019s \u201cRicerca Corrente\u201d funding to the IRCCS Humanitas Research Hospital"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The accurate prediction of response to neoadjuvant therapy (NAT) is crucial for optimizing breast cancer management. Conventional breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) radiomics typically relies on single post-contrast phases and may not fully capture temporal enhancement patterns related to tumor heterogeneity. This study evaluated a machine learning model based on time-dependent radiomic features extracted from pre-treatment DCE-MRI for predicting NAT response in breast cancer patients. Methods: Breast DCE-MRI examinations of women scheduled for NAT, acquired on 1.5 T scanners from three different vendors, were retrospectively collected from two centers. Tumors were automatically segmented on the third post-contrast DCE image using a 3D nnUNet model trained on 30 lesions. All DCE phases were registered to the reference image, and radiomic features were extracted from a consistent tumor region of interest across all phases. Time-dependent radiomic features were computed using linear regression modeling of feature evolution over time. A random forest classifier integrating static and time-dependent radiomic features was developed to predict pathological complete response (pCR), partial response (pPR), and non-response (pNR). Model performance was evaluated using internal validation (Center 1) and an independent external test cohort (Center 2). Results: A total of 212 patients were included (173 from Center 1 and 39 from Center 2), comprising 103 pCR, 103 pPR and 6 pNR cases. Among 759 extracted features, 30 showed significant differences across response groups. Several time-dependent texture features related to intratumoral heterogeneity were significantly associated with pNR. The model achieved AUC values of 0.80, 0.81, and 0.95 in the internal validation cohort and 0.75, 0.74, and 0.86 in the external test cohort for predicting pCR, pPR, and pNR, respectively. Conclusions: Time-dependent radiomic features derived from pre-treatment breast DCE-MRI enable the accurate prediction of response to NAT, with particularly strong performance in identifying non-responders. This approach may support imaging-based risk stratification and contribute to more personalized treatment.<\/jats:p>","DOI":"10.3390\/diagnostics16040611","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:47:02Z","timestamp":1771508822000},"page":"611","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Time-Dependent DCE-MRI Radiomics to Predict Response to Neoadjuvant Therapy in Breast Cancer: A Multicenter Study with External Validation"],"prefix":"10.3390","volume":"16","author":[{"given":"Giulia","family":"Vatteroni","sequence":"first","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via R. Montalcini 4, 20072 Pieve Emanuele, Italy"},{"name":"Radiology Department, IRCCS Humanitas Research Hospital, Via A. Manzoni 56, 20089 Rozzano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9030-0071","authenticated-orcid":false,"given":"Riccardo","family":"Levi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via R. Montalcini 4, 20072 Pieve Emanuele, Italy"},{"name":"Radiology Department, IRCCS Humanitas Research Hospital, Via A. Manzoni 56, 20089 Rozzano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paola","family":"Nardi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via R. Montalcini 4, 20072 Pieve Emanuele, Italy"},{"name":"Radiology Department, IRCCS Humanitas Research Hospital, Via A. Manzoni 56, 20089 Rozzano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Pruneddu","sequence":"additional","affiliation":[{"name":"Radiology Department, IRCCS Humanitas Research Hospital, Via A. Manzoni 56, 20089 Rozzano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisa","family":"Salpietro","sequence":"additional","affiliation":[{"name":"Radiology Department, Humanitas Gavazzeni, Via Mauro Gavazzeni 21, 24125 Bergamo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federica","family":"Fici","sequence":"additional","affiliation":[{"name":"Radiology Department, Humanitas Gavazzeni, Via Mauro Gavazzeni 21, 24125 Bergamo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cinzia","family":"Monti","sequence":"additional","affiliation":[{"name":"Radiology Department, Humanitas Gavazzeni, Via Mauro Gavazzeni 21, 24125 Bergamo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rubina Manuela","family":"Trimboli","sequence":"additional","affiliation":[{"name":"Radiology Department, IRCCS Humanitas Research Hospital, Via A. Manzoni 56, 20089 Rozzano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4329-8121","authenticated-orcid":false,"given":"Daniela","family":"Bernardi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via R. Montalcini 4, 20072 Pieve Emanuele, Italy"},{"name":"Radiology Department, IRCCS Humanitas Research Hospital, Via A. Manzoni 56, 20089 Rozzano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1002\/jso.21696","article-title":"Neoadjuvant Systemic Treatment of Breast Cancer","volume":"103","author":"Makhoul","year":"2011","journal-title":"J. Surg. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1200\/JCO.2005.02.6187","article-title":"Recommendations From an International Expert Panel on the Use of Neoadjuvant (Primary) Systemic Treatment of Operable Breast Cancer: An Update","volume":"24","author":"Kaufmann","year":"2006","journal-title":"J. Clin. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1178223420980377","article-title":"Neoadjuvant Chemotherapy for Breast Cancer: Past, Present, and Future","volume":"14","author":"Asaoka","year":"2020","journal-title":"Breast Cancer Basic Clin. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1148\/rg.2021200134","article-title":"Breast MRI for Evaluation of Response to Neoadjuvant Therapy","volume":"41","author":"Reig","year":"2021","journal-title":"RadioGraphics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1200\/JCO.2011.38.8595","article-title":"Definition and Impact of Pathologic Complete Response on Prognosis After Neoadjuvant Chemotherapy in Various Intrinsic Breast Cancer Subtypes","volume":"30","author":"Untch","year":"2012","journal-title":"J. Clin. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s12282-013-0474-2","article-title":"Prognostic Significance of Pathologic Complete Response and Ki67 Expression after Neoadjuvant Chemotherapy in Breast Cancer","volume":"22","author":"Yoshioka","year":"2015","journal-title":"Breast Cancer"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1001\/jamaoncol.2015.6113","article-title":"Association of Pathologic Complete Response to Neoadjuvant Therapy in HER2-Positive Breast Cancer with Long-Term Outcomes: A Meta-Analysis","volume":"2","author":"Broglio","year":"2016","journal-title":"JAMA Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.breast.2023.03.016","article-title":"Pathologic Response and Residual Tumor Cellularity after Neo-Adjuvant Chemotherapy Predict Prognosis in Breast Cancer Patients","volume":"69","author":"Gentile","year":"2023","journal-title":"Breast"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1016\/S1470-2045(18)30570-9","article-title":"Neoadjuvant Chemotherapy with or without Anthracyclines in the Presence of Dual HER2 Blockade for HER2-Positive Breast Cancer (TRAIN-2): A Multicentre, Open-Label, Randomised, Phase 3 Trial","volume":"19","author":"Mandjes","year":"2018","journal-title":"Lancet Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1093\/annonc\/mdx494","article-title":"De-Escalation Strategies in HER2-Positive Early Breast Cancer (EBC): Final Analysis of the WSG-ADAPT HER2+\/HR\u2212 Phase II Trial: Efficacy, Safety, and Predictive Markers for 12 Weeks of Neoadjuvant Dual Blockade with Trastuzumab and Pertuzumab \u00b1 Weekly Paclitaxel","volume":"28","author":"Nitz","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"S63","DOI":"10.3816\/CBC.2002.s.014","article-title":"Primary Chemotherapy in the Treatment of Breast Cancer: The University of Texas M. D. Anderson Cancer Center Experience","volume":"3","author":"Valero","year":"2002","journal-title":"Clin. Breast Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"536145","DOI":"10.1155\/2015\/536145","article-title":"Assessment of Pathological Response of Breast Carcinoma in Modified Radical Mastectomy Specimens after Neoadjuvant Chemotherapy","volume":"2015","author":"Vasudevan","year":"2015","journal-title":"Int. J. Breast Cancer"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1097\/RLI.0000000000001010","article-title":"Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction","volume":"59","author":"Marcus","year":"2024","journal-title":"Investig. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1093\/jnci\/djs528","article-title":"Meta-Analysis of Magnetic Resonance Imaging in Detecting Residual Breast Cancer After Neoadjuvant Therapy","volume":"105","author":"Marinovich","year":"2013","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"691","DOI":"10.6004\/jnccn.2022.0030","article-title":"Breast Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology","volume":"20","author":"Gradishar","year":"2022","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ejca.2008.10.026","article-title":"New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1)","volume":"45","author":"Eisenhauer","year":"2009","journal-title":"Eur. J. Cancer"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"16372","DOI":"10.1038\/s41598-019-52785-3","article-title":"Diffusion-Weighted Magnetic Resonance Imaging of Patients with Breast Cancer Following Neoadjuvant Chemotherapy Provides Early Prediction of Pathological Response\u2014A Prospective Study","volume":"9","author":"Pereira","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1148\/radiol.2018180273","article-title":"Diffusion-Weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial","volume":"289","author":"Partridge","year":"2018","journal-title":"Radiology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.ejrad.2019.06.009","article-title":"Multiparametric MR Imaging to Assess Response Following Neoadjuvant Systemic Treatment in Various Breast Cancer Subtypes: Comparison between Different Definitions of Pathologic Complete Response","volume":"117","author":"Ganau","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.ejrad.2025.112105","article-title":"Role of Imaging Based Response Assesment for Adapting Neoadjuvant Systemic Therapy for Breast Cancer: A Systematic Review","volume":"187","author":"Lafci","year":"2025","journal-title":"Eur. J. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pesapane, F., Rotili, A., Botta, F., Raimondi, S., Bianchini, L., Corso, F., Ferrari, F., Penco, S., Nicosia, L., and Bozzini, A. (2021). Radiomics of MRI for the Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Single Referral Centre Analysis. Cancers, 13.","DOI":"10.3390\/cancers13174271"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1002\/jmri.26852","article-title":"Machine Learning in Breast MRI","volume":"52","author":"Reig","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104681","DOI":"10.1016\/j.critrevonc.2025.104681","article-title":"Incorporating Radiomic MRI Models for Presurgical Response Assessment in Patients with Early Breast Cancer Undergoing Neoadjuvant Systemic Therapy: Collaborative Insights from Breast Oncologists and Radiologists","volume":"210","author":"Gaudio","year":"2025","journal-title":"Crit. Rev. Oncol.\/Hematol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100883","DOI":"10.1016\/j.currproblcancer.2022.100883","article-title":"Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients with MRI-Radiomics: A Systematic Review and Meta-Analysis","volume":"46","author":"Pesapane","year":"2022","journal-title":"Curr. Probl. Cancer"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110247","DOI":"10.1016\/j.ejrad.2022.110247","article-title":"Machine Learning with Magnetic Resonance Imaging for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Systematic Review and Meta-Analysis","volume":"150","author":"Liang","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"280","DOI":"10.2214\/AJR.18.20389","article-title":"Artificial Intelligence for Breast MRI in 2008\u20132018: A Systematic Mapping Review","volume":"212","author":"Codari","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"6520","DOI":"10.1118\/1.4933198","article-title":"Computer-aided Breast MR Image Feature Analysis for Prediction of Tumor Response to Chemotherapy","volume":"42","author":"Aghaei","year":"2015","journal-title":"Med. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s11547-025-01984-2","article-title":"Role of Radiomics in Predicting Early Disease Recurrence in Locally Advanced Breast Cancer Patients: Integration of Radiomic Features and RECIST Criteria","volume":"130","author":"Trombadori","year":"2025","journal-title":"Radiol. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.3390\/tomography8060232","article-title":"Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review","volume":"8","author":"Khan","year":"2022","journal-title":"Tomography"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.mri.2019.07.008","article-title":"Accuracy of Multi-Parametric Breast MR Imaging for Predicting Pathological Complete Response of Operable Breast Cancer Prior to Neoadjuvant Systemic Therapy","volume":"62","author":"Tsukada","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Herrero Vicent, C., Tudela, X., Moreno Ruiz, P., Pedralva, V., Jim\u00e9nez Pastor, A., Ahicart, D., Rubio Novella, S., Meneu, I., Montes Albuixech, \u00c1., and Santamaria, M.\u00c1. (2022). Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers, 14.","DOI":"10.3390\/cancers14143508"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1002\/jmri.22567","article-title":"The Diverse Pathology and Kinetics of Mass, Nonmass, and Focus Enhancement on MR Imaging of the Breast","volume":"33","author":"Jansen","year":"2011","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/S1470-2045(24)00104-9","article-title":"MRI-Guided Optimisation of Neoadjuvant Chemotherapy Duration in Stage II\u2013III HER2-Positive Breast Cancer (TRAIN-3): A Multicentre, Single-Arm, Phase 2 Study","volume":"25","author":"Louis","year":"2024","journal-title":"Lancet Oncol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13244-023-01415-8","article-title":"CheckList for EvaluAtion of Radiomics Research (CLEAR): A Step-by-Step Reporting Guideline for Authors and Reviewers Endorsed by ESR and EuSoMII","volume":"14","author":"Kocak","year":"2023","journal-title":"Insights Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s13058-018-0950-x","article-title":"MRI-Based Response Patterns during Neoadjuvant Chemotherapy Can Predict Pathological (Complete) Response in Patients with Breast Cancer","volume":"20","author":"Goorts","year":"2018","journal-title":"Breast Cancer Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1111\/j.1365-2559.2006.02419.x","article-title":"Laboratory Handling and Histology Reporting of Breast Specimens from Patients Who Have Received Neoadjuvant Chemotherapy","volume":"50","author":"Pinder","year":"2007","journal-title":"Histopathology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","article-title":"Pathological Complete Response and Long-Term Clinical Benefit in Breast Cancer: The CTNeoBC Pooled Analysis","volume":"384","author":"Cortazar","year":"2014","journal-title":"Lancet"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1200\/JCO.20.03399","article-title":"Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline","volume":"39","author":"Korde","year":"2021","journal-title":"J. Clin. Oncol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gerosa, R., Gelardi, F., Tiberio, P., Jacobs, F., Benvenuti, C., Gaudio, M., Canzian, J., Tinterri, B., Zambelli, A., and Santoro, A. (2025). [18F]FDG PET\/CT as a Biomarker for Response Evaluation in Neoadjuvant Treatment of Early Breast Cancer: Could It Become a Game-Changer in the Scenario of the Emerging (de)-Escalation Strategies?. Radiol. Med., Epub ahead of printing.","DOI":"10.1007\/s11547-025-02138-0"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1158\/1078-0432.CCR-18-3190","article-title":"Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study","volume":"25","author":"Liu","year":"2019","journal-title":"Clin. Cancer Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s12094-019-02109-8","article-title":"Multiparametric MRI-Based Radiomics Analysis for Prediction of Breast Cancers Insensitive to Neoadjuvant Chemotherapy","volume":"22","author":"Xiong","year":"2020","journal-title":"Clin. Transl. Oncol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s10549-018-4990-9","article-title":"Multivariate Machine Learning Models for Prediction of Pathologic Response to Neoadjuvant Therapy in Breast Cancer Using MRI Features: A Study Using an Independent Validation Set","volume":"173","author":"Cain","year":"2019","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100831","DOI":"10.1016\/j.tranon.2020.100831","article-title":"Multiparametric MRI-Based Radiomics Analysis for the Prediction of Breast Tumor Regression Patterns after Neoadjuvant Chemotherapy","volume":"13","author":"Zhuang","year":"2020","journal-title":"Transl. Oncol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"S145","DOI":"10.1016\/j.acra.2020.10.020","article-title":"MRI Radiomics for Assessment of Molecular Subtype, Pathological Complete Response, and Residual Cancer Burden in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy","volume":"29","author":"Choudhery","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cai, J., Cui, C., Qi, S., and Zhao, D. (2025). Predicting Breast Cancer Response to Neoadjuvant Therapy by Integrating Radiomic and Deep-Learning Features from Early-and-Peak Phases of DCE-MRI. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-15095-8"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1001\/jama.2013.278932","article-title":"Sentinel Lymph Node Surgery After Neoadjuvant Chemotherapy in Patients with Node-Positive Breast Cancer: The ACOSOG Z1071 (Alliance) Clinical Trial","volume":"310","author":"Boughey","year":"2013","journal-title":"JAMA"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3501","DOI":"10.1245\/s10434-016-5277-1","article-title":"Nomograms for Predicting Axillary Response to Neoadjuvant Chemotherapy in Clinically Node-Positive Patients with Breast Cancer","volume":"23","author":"Vila","year":"2016","journal-title":"Ann. Surg. Oncol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.diii.2018.09.001","article-title":"Enhancement of Breast Cancer on Pre-Treatment Dynamic Contrast-Enhanced MRI Using Computer-Aided Detection Is Associated with Response to Neo-Adjuvant Chemotherapy","volume":"99","author":"Ueno","year":"2018","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e222830","DOI":"10.1148\/radiol.222830","article-title":"MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer","volume":"308","author":"Shi","year":"2023","journal-title":"Radiology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nrclinonc.2015.73","article-title":"Clinical Management of Breast Cancer Heterogeneity","volume":"12","author":"Zardavas","year":"2015","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1038\/s41416-021-01328-7","article-title":"Breast Cancer as an Example of Tumour Heterogeneity and Tumour Cell Plasticity during Malignant Progression","volume":"125","author":"Tiede","year":"2021","journal-title":"Br. J. Cancer"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"S135","DOI":"10.1016\/j.acra.2020.12.001","article-title":"Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non\u2013Fat-Sat Images and Tested on Fat-Sat Images","volume":"29","author":"Zhang","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.acra.2021.08.024","article-title":"3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients","volume":"29","author":"Militello","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1002\/nbm.3132","article-title":"Dynamic Contrast-enhanced MRI Texture Analysis for Pretreatment Prediction of Clinical and Pathological Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Breast Cancer","volume":"27","author":"Teruel","year":"2014","journal-title":"NMR Biomed."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/4\/611\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:50:27Z","timestamp":1771509027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/4\/611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["diagnostics16040611"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16040611","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]}}}