{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T05:04:44Z","timestamp":1789275884590,"version":"build-2803163510"},"reference-count":156,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Fujian Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["2025J01103"],"award-info":[{"award-number":["2025J01103"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Neoadjuvant therapy for breast cancer is planned by subtype: pathologic complete response (pCR) differs in frequency, meaning, and surrogate validity across HR+\/HER2\u2212, HER2+, and triple-negative breast cancer (TNBC). This review examines whether current evidence supports using artificial intelligence (AI) to move beyond predicting response under a fixed regimen to guiding systemic treatment tailoring. Here, tailoring means model-guided drug omission, switching, escalation, or de-escalation for an individual patient. We appraised 102 full-text studies of AI-based response prediction (2020\u20132026), organized by subtype and clinical decision, and graded each on an author-defined five-level clinical-readiness ladder (L1\u2013L5) measuring validation and translational maturity rather than accuracy. Risk of bias was assessed with PROBAST and reporting against TRIPOD+AI. Readiness clustered low: 43 studies reached internal validation only (L1), 53 temporal or geographic external validation (L2), and 6 prospective observational validation (L3); none reached workflow integration (L4) or interventional evidence (L5). Most carried high overall risk of bias (89\/102); external validation appeared in 52 (51%), fully reported decision-curve analysis in 44 (43%), and calibration in 19 (19%). Strategy comparison and individualized-treatment-effect analyses were essentially absent. Subtype-specific AI currently predicts response under fixed regimens with moderate-to-good discrimination; the evidence does not yet support changing an individual patient\u2019s systemic treatment.<\/jats:p>","DOI":"10.3390\/cancers18182947","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T08:17:42Z","timestamp":1789114662000},"page":"2947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy"],"prefix":"10.3390","volume":"18","author":[{"given":"Junwen","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Xi","sequence":"additional","affiliation":[{"name":"Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0570-2932","authenticated-orcid":false,"given":"Kehuan","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linying","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1056\/NEJMoa1814017","article-title":"Trastuzumab Emtansine for Residual Invasive HER2-Positive Breast Cancer (KATHERINE)","volume":"380","author":"Huang","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1056\/NEJMoa2112651","article-title":"Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer (KEYNOTE-522)","volume":"386","author":"Schmid","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1007\/s00330-025-11801-z","article-title":"Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial","volume":"36","author":"Janse","year":"2026","journal-title":"Eur. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/S1470-2045(25)00727-2","article-title":"Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: A multicentre, model development and validation study","volume":"27","author":"Shamai","year":"2026","journal-title":"Lancet Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1038\/s41392-026-02734-0","article-title":"Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer","volume":"11","author":"Wu","year":"2026","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"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":"e240300","DOI":"10.1148\/ryai.240300","article-title":"CLAIM 2024 Update Panel. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting Guideline for the Early-Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence: DECIDE-AI","volume":"28","author":"Vasey","year":"2022","journal-title":"Nat. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105896","DOI":"10.1016\/j.ebiom.2025.105896","article-title":"Deep learning-based prediction of axillary pathological complete response in patients with breast cancer using longitudinal multiregional ultrasound","volume":"119","author":"Liu","year":"2025","journal-title":"EBioMedicine"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1186\/s13058-023-01688-3","article-title":"Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: A multicenter cohort study","volume":"25","author":"Yu","year":"2023","journal-title":"Breast Cancer Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s13058-022-01516-0","article-title":"Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment","volume":"24","author":"Wang","year":"2022","journal-title":"Breast Cancer Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1007\/s12282-026-01851-6","article-title":"Low-progesterone receptor expression predicts nodal pathological complete response after neoadjuvant chemotherapy in premenopausal patients with ER-positive\/HER2-negative breast cancer","volume":"33","author":"Iwamoto","year":"2026","journal-title":"Breast Cancer"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41586-021-04278-5","article-title":"Multi-omic machine learning predictor of breast cancer therapy response","volume":"601","author":"Sammut","year":"2022","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, C., Yu, T., Yang, L., He, L., Zhu, J., and Chen, J. (2025). Clinical prediction of pathological complete response in breast cancer: A machine learning study. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14335-1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, Q., Yin, W., Yang, L., Xiao, B., Wang, J., and Yao, X. (2023). Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients. BMC Cancer, 23.","DOI":"10.1186\/s12885-023-10817-2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s41698-023-00352-5","article-title":"Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images","volume":"7","author":"Huang","year":"2023","journal-title":"npj Precis. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1186\/s12967-023-04713-3","article-title":"Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer","volume":"21","author":"Omar","year":"2023","journal-title":"J. Transl. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s40644-024-00746-z","article-title":"A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy","volume":"24","author":"Zhou","year":"2024","journal-title":"Cancer Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e2413702","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_24","doi-asserted-by":"crossref","first-page":"9613","DOI":"10.1038\/s41467-024-53450-8","article-title":"An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer","volume":"15","author":"Gao","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1038\/s41416-024-02726-3","article-title":"A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: A secondary analysis to multicenter clinical trial","volume":"131","author":"Zhu","year":"2024","journal-title":"Br. J. Cancer"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8351","DOI":"10.1038\/s41598-025-91888-y","article-title":"TNMpBC-NeoBCSS model: A breast cancer specific survival prediction model for triple-negative metaplastic breast carcinoma patients with neoadjuvant therapy","volume":"15","author":"Ni","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"eadr1576","DOI":"10.1126\/sciadv.adr1576","article-title":"A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer","volume":"11","author":"Mao","year":"2025","journal-title":"Sci. Adv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104543","DOI":"10.1016\/j.breast.2025.104543","article-title":"Effect of a machine learning prediction model on the false-negative rate of sentinel lymph node biopsy for clinically node-positive breast cancer after neoadjuvant chemotherapy","volume":"83","author":"Chen","year":"2025","journal-title":"Breast"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1097\/JS9.0000000000003326","article-title":"Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence","volume":"112","author":"Zhou","year":"2026","journal-title":"Int. J. Surg."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104069","DOI":"10.1016\/j.media.2026.104069","article-title":"Predicting neoadjuvant therapy response in breast cancer from preoperative biopsy via spatial-semantic-differential learning and interpretable clinicopathological-guided fusion","volume":"111","author":"Hou","year":"2026","journal-title":"Med. Image Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7193","DOI":"10.1038\/s41467-026-73617-9","article-title":"Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer","volume":"17","author":"Azimzade","year":"2026","journal-title":"Nat. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.annonc.2026.05.700","article-title":"Predicting neoadjuvant breast cancer therapy response using BRIDGE from tumor transcriptomics and histopathology","volume":"37","author":"Cantore","year":"2026","journal-title":"Ann. Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4225","DOI":"10.1016\/j.cell.2026.04.023","article-title":"AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology","volume":"189","author":"Shulman","year":"2026","journal-title":"Cell"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"F\u00fcrb\u00f6ck, C., Janickova, I., Langs, G., Helbich, T.H., Clauser, P., Varga, R., Baltzer, P., and Kapetas, P. (2026). Deep-Learning with Domain-Specific Pretraining for Breast Cancer Neoadjuvant Chemotherapy Response Prediction from Pre-Treatment B-Mode Ultrasound. Cancers, 18.","DOI":"10.3390\/cancers18091345"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105311","DOI":"10.1016\/j.ebiom.2024.105311","article-title":"Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: Multicentre study","volume":"107","author":"Li","year":"2024","journal-title":"EBioMedicine"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, X., Luo, Y., Xie, Z., Wen, Y., Mou, F., and Zeng, W. (2025). Prediction of neoadjuvant chemotherapy efficacy in breast cancer: Integrating multimodal imaging and clinical features. BMC Med. Imaging, 25.","DOI":"10.1186\/s12880-025-01631-2"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1097\/SLA.0000000000006279","article-title":"Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden During Neoadjuvant Chemotherapy in Breast Cancer","volume":"281","author":"Li","year":"2025","journal-title":"Ann. Surg."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s40644-025-00934-5","article-title":"Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics","volume":"25","author":"Peng","year":"2025","journal-title":"Cancer Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1093\/bjr\/tqae124","article-title":"Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers","volume":"97","author":"Huang","year":"2024","journal-title":"Br. J. Radiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3987","DOI":"10.1200\/JCO.20.02514","article-title":"Abemaciclib Combined With Endocrine Therapy for the Adjuvant Treatment of HR+, HER2-, Node-Positive, High-Risk, Early Breast Cancer (monarchE)","volume":"38","author":"Johnston","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1056\/NEJMoa2305488","article-title":"Ribociclib plus Endocrine Therapy in Early Breast Cancer (NATALEE)","volume":"390","author":"Slamon","year":"2024","journal-title":"N. Engl. J. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/S1470-2045(20)30458-7","article-title":"Long-term outcome and prognostic value of Ki67 after perioperative endocrine therapy in postmenopausal women with hormone-sensitive early breast cancer (POETIC): An open-label, multicentre, parallel-group, randomised, phase 3 trial","volume":"21","author":"Smith","year":"2020","journal-title":"Lancet Oncol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1093\/jnci\/djn309","article-title":"Outcome prediction for estrogen receptor-positive breast cancer based on postneoadjuvant endocrine therapy tumor characteristics (PEPI)","volume":"100","author":"Ellis","year":"2008","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4890","DOI":"10.1245\/s10434-025-17297-7","article-title":"Long-term Outcomes for Neoadjuvant Versus Adjuvant Chemotherapy in ER+\/HER2- Locally Advanced Breast Cancer","volume":"32","author":"Wang","year":"2025","journal-title":"Ann. Surg. Oncol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/s13058-024-01905-7","article-title":"Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach","volume":"26","author":"Zhao","year":"2024","journal-title":"Breast Cancer Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13058-025-02181-9","article-title":"Attention-based multimodal fusion transformer for predicting the efficacy of neoadjuvant therapy in breast cancer: A cross-institutional retrospective study","volume":"28","author":"Zhang","year":"2025","journal-title":"Breast Cancer Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13058-025-01971-5","article-title":"Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model","volume":"27","author":"Feng","year":"2025","journal-title":"Breast Cancer Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zeng, H., Qiu, S., Zhuang, S., Wei, X., Wu, J., Zhang, R., Chen, K., Wu, Z., and Zhuang, Z. (2024). Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: A multicenter study. Front. Physiol., 15.","DOI":"10.3389\/fphys.2024.1279982"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, W., Onishi, N., Gibbs, J.E., Wilmes, L.J., Le, N.N., Metanat, P., Price, E.R., Joe, B.N., Kornak, J., and Yau, C. (2025). MRI-Based Model for Personalizing Neoadjuvant Treatment in Breast Cancer. Tomography, 11.","DOI":"10.3390\/tomography11030026"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yu, B., Su, M., Yuan, C., Liu, C., Wang, X., Song, X., Li, C., Wang, F., and Ma, J. (2023). Construction of a risk stratification model integrating ctDNA to predict response and survival in neoadjuvant-treated breast cancer. BMC Med., 21.","DOI":"10.1186\/s12916-023-03163-4"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, M., Liu, H., Dai, Q., Yao, L., Zhang, S., Wang, Z., Li, J., and Duan, Q. (2022). Treatment Response Prediction Using Ultrasound-Based Pre-, Post-Early, and Delta Radiomics in Neoadjuvant Chemotherapy in Breast Cancer. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.748008"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1038\/s41419-025-07877-6","article-title":"Multi-omics prediction of axillary treatment response and tumour microenvironment alterations in lymph node-positive luminal breast cancer","volume":"16","author":"Ma","year":"2025","journal-title":"Cell Death Dis."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"103801","DOI":"10.1016\/j.ebiom.2021.103801","article-title":"Development and validation of the new HER2DX assay for predicting pathological response and survival outcome in early-stage HER2-positive breast cancer","volume":"75","author":"Prat","year":"2022","journal-title":"EBioMedicine"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1097\/JS9.0000000000003931","article-title":"A prospective single-center cohort study: Integrating PET imaging and genomics to identify early biomarkers of response to dual-targeted PH neoadjuvant therapy in breast cancer","volume":"112","author":"Yang","year":"2026","journal-title":"Int. J. Surg."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s12967-025-07608-7","article-title":"Focal hotspot and diffuse immune subtypes of tumor-infiltrating lymphocytes: AI-powered spatial clustering classification and its clinical relevance to HER2 expression in triple-negative breast cancer","volume":"24","author":"Xie","year":"2025","journal-title":"J. Transl. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1038\/s41598-023-27518-2","article-title":"Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI","volume":"13","author":"Zhou","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13058-023-01752-y","article-title":"Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer","volume":"26","author":"Fisher","year":"2024","journal-title":"Breast Cancer Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"eaec2358","DOI":"10.1126\/scitranslmed.aec2358","article-title":"Peripheral blood transcriptional profiling predicts tumor subtype and neoadjuvant chemoimmunotherapy outcomes in human breast cancer","volume":"18","author":"Sun","year":"2026","journal-title":"Sci. Transl. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e2500410","DOI":"10.1200\/PO-25-00410","article-title":"Imaging- and Tumor Biomarker-Based Multivariable Model for Early Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer","volume":"9","author":"Adrada","year":"2025","journal-title":"JCO Precis. Oncol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1056\/NEJMoa1513750","article-title":"Adaptive Randomization of Neratinib in Early Breast Cancer (I-SPY2)","volume":"375","author":"Park","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1002\/jmri.28679","article-title":"Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study","volume":"58","author":"Janse","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.1002\/cncr.32830","article-title":"A nomogram for predicting axillary pathologic complete response in hormone receptor-positive breast cancer with cytologically proven axillary lymph node metastases","volume":"126","author":"Guo","year":"2020","journal-title":"Cancer"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1186\/s13550-021-00861-z","article-title":"Value of 18F-FDG PET\/CT for predicting axillary pathologic complete response following neoadjuvant systemic therapy in breast cancer patients: Emphasis on breast cancer subtype","volume":"11","author":"Mitea","year":"2021","journal-title":"EJNMMI Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"14123","DOI":"10.1038\/s41598-021-93592-z","article-title":"Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs","volume":"11","author":"Comes","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s13058-025-02034-5","article-title":"Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: A retrospective, multicenter study","volume":"27","author":"Huang","year":"2025","journal-title":"Breast Cancer Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s13058-024-01836-3","article-title":"Enhancing pathological complete response prediction in breast cancer: The role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity","volume":"26","author":"Zhang","year":"2024","journal-title":"Breast Cancer Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e70100","DOI":"10.1111\/cpr.70100","article-title":"Endocrine Resistance Score Based on Three Key Genes Predicts Prognosis and Reveals Potential Therapeutic Targets for ER-positive\/HER2-negative Breast Cancer","volume":"59","author":"Ping","year":"2026","journal-title":"Cell Prolif."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.34133\/research.1303","article-title":"Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy Decision-Making in Luminal Breast Cancer","volume":"9","author":"Liang","year":"2026","journal-title":"Research"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wang, K.N., Meng, Y.J., Yu, Y., Cai, W.R., Wang, X., Cao, X.C., and Ge, J. (2023). Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1117538"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e240769","DOI":"10.1148\/ryai.240769","article-title":"Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI","volume":"7","author":"Wu","year":"2025","journal-title":"Radiol. Artif. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sun, Y., Liao, Q., Fan, Y., Cui, C., Wang, Y., Yang, C., Hou, Y., and Zhao, D. (2025). DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14004-3"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"101899","DOI":"10.1016\/j.eclinm.2023.101899","article-title":"Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: A multicenter, retrospective study","volume":"58","author":"Huang","year":"2023","journal-title":"EClinicalMedicine"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"984","DOI":"10.21147\/j.issn.1000-9604.2025.06.10","article-title":"A multicenter study of a predictive model for pathological complete response after neoadjuvant therapy in breast cancer using multimodal digital biomarkers","volume":"37","author":"Zixuan","year":"2025","journal-title":"Chin. J. Cancer Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1186\/s13058-022-01580-6","article-title":"An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: A multicentre, retrospective study","volume":"24","author":"Wu","year":"2022","journal-title":"Breast Cancer Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/s13058-023-01726-0","article-title":"PROACTING: Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning","volume":"25","author":"Aswolinskiy","year":"2023","journal-title":"Breast Cancer Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.breast.2022.10.004","article-title":"Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: A multicenter study","volume":"66","author":"Li","year":"2022","journal-title":"Breast"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1038\/s41551-025-01495-5","article-title":"Deep-learning-based HER2 status assessment from multimodal breast cancer data predicts neoadjuvant therapy response","volume":"10","author":"Zhang","year":"2026","journal-title":"Nat. Biomed. Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"101562","DOI":"10.1016\/j.eclinm.2022.101562","article-title":"Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study","volume":"52","author":"Liu","year":"2022","journal-title":"EClinicalMedicine"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1097\/JCMA.0000000000001034","article-title":"Preoperative systemic inflammation response index: Clinicopathologic predictor of pathological complete response in HER2-positive breast cancer patients receiving neoadjuvant systemic therapy","volume":"87","author":"Wu","year":"2024","journal-title":"J. Chin. Med. Assoc."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S1470-2045(15)00551-3","article-title":"Neratinib after trastuzumab-based adjuvant therapy in patients with HER2-positive breast cancer (ExteNET): A multicentre, randomised, double-blind, placebo-controlled, phase 3 trial","volume":"17","author":"Chan","year":"2016","journal-title":"Lancet Oncol."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zeng, T., He, Y., Zhang, T., Ren, C., Xu, J., Cheng, J., and Ming, W. (2026). Dedicated Breast PET-Based Deep Learning Radiomics for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer. Cancers, 18.","DOI":"10.3390\/cancers18101581"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"23065","DOI":"10.1038\/s41598-024-74276-w","article-title":"Deep-learning based discrimination of pathologic complete response using MRI in HER2-positive and triple-negative breast cancer","volume":"14","author":"Kim","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1056\/NEJMoa1612645","article-title":"Adjuvant Capecitabine for Breast Cancer after Preoperative Chemotherapy","volume":"376","author":"Masuda","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2394","DOI":"10.1056\/NEJMoa2105215","article-title":"Adjuvant Olaparib for Patients with BRCA1- or BRCA2-Mutated Breast Cancer (OlympiA)","volume":"384","author":"Tutt","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1038\/s41523-026-00961-w","article-title":"AI-powered and manual assessment of tumor-infiltrating lymphocytes in early HER2-positive breast cancer in NSABP B-41","volume":"12","author":"Schlam","year":"2026","journal-title":"npj Breast Cancer"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1002\/path.70022","article-title":"Integration of histopathological characteristics by machine learning improves the prediction of neoadjuvant immunochemotherapy response in triple-negative breast cancer","volume":"268","author":"Lu","year":"2026","journal-title":"J. Pathol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/j.annonc.2026.04.011","article-title":"Including tumor-infiltrating lymphocytes into the PREDICT prognostic model for triple-negative breast cancer survival","volume":"37","author":"Wang","year":"2026","journal-title":"Ann. Oncol."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Yan, L., Huang, X., Liu, L., Wu, A., Luo, Y., Li, H., Yi, S., Yu, T., and Zeng, Q. (2026). Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data. Front. Immunol., 17.","DOI":"10.3389\/fimmu.2026.1749668"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"936","DOI":"10.7150\/jca.52439","article-title":"Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple-negative breast cancer","volume":"12","author":"Han","year":"2021","journal-title":"J. Cancer"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Liang, H., Zhu, H., Wang, L., Yu, J., Feng, R., Yan, X., Shi, L., Xiao, J., Zhou, H., and Chen, P. (2026). AI-driven spatial analysis of tumor-infiltrating lymphocytes predicts chemo-immunotherapy response in triple-negative breast cancer. Front. Immunol., 17.","DOI":"10.3389\/fimmu.2026.1797753"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1186\/s12967-025-06369-7","article-title":"Intratumoral microbiota-aided fusion radiomics model for predicting tumor response to neoadjuvant chemoimmunotherapy in triple-negative breast cancer","volume":"23","author":"Chen","year":"2025","journal-title":"J. Transl. Med."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Groheux, D., Ferrer, L., Vargas, J., Martineau, A., Borgel, A., Teixeira, L., Menu, P., Bertheau, P., Gallinato, O., and Colin, T. (2025). FDG-PET\/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Cancers, 17.","DOI":"10.3390\/cancers17071249"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"e243824","DOI":"10.1148\/radiol.243824","article-title":"Posttreatment MRI to Predict Pathologic Complete Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy","volume":"316","author":"Ramtohul","year":"2025","journal-title":"Radiology"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"e240124","DOI":"10.1148\/ryai.240124","article-title":"Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer","volume":"7","author":"Stowers","year":"2025","journal-title":"Radiol. Artif. Intell."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"103445","DOI":"10.1016\/j.artmed.2026.103445","article-title":"Evidential reasoning-enabled deep learning for reliable treatment outcome prediction in cancer therapy","volume":"178","author":"Chen","year":"2026","journal-title":"Artif. Intell. Med."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Xia, B., Wang, H., Wang, Z., Qian, Z., Xiao, Q., Liu, Y., Shao, Z., Zhou, S., Chai, W., and You, C. (2021). A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy. Front. Genet., 12.","DOI":"10.3389\/fgene.2021.783513"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ejca.2020.08.020","article-title":"Immunosuppressive profiles in liquid biopsy at diagnosis predict response to neoadjuvant chemotherapy in triple-negative breast cancer","volume":"139","author":"Santaballa","year":"2020","journal-title":"Eur. J. Cancer"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1007\/s00330-023-10238-6","article-title":"Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?","volume":"34","author":"Cai","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Ye, Z., Yuan, J., Hong, D., Xu, P., and Liu, W. (2025). Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer. Front. Immunol., 16.","DOI":"10.3389\/fimmu.2025.1559200"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"9385","DOI":"10.1002\/mp.17451","article-title":"Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification","volume":"51","author":"Jing","year":"2024","journal-title":"Med. Phys."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/s00259-023-06513-9","article-title":"Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast 18F-FDG-PET\/MRI","volume":"51","author":"Jannusch","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"21149","DOI":"10.1038\/s41598-020-77875-5","article-title":"Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET\/MRI image deep learning","volume":"10","author":"Choi","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Yang, L., Chang, J., He, X., Peng, M., Zhang, Y., Wu, T., Xu, P., Chu, W., Gao, C., and Cao, S. (2022). PET\/CT-based radiomics analysis may help to predict neoadjuvant chemotherapy outcomes in breast cancer. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.849626"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"e24371","DOI":"10.1016\/j.heliyon.2024.e24371","article-title":"Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer","volume":"10","author":"Xu","year":"2024","journal-title":"Heliyon"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"0212","DOI":"10.34133\/bmef.0212","article-title":"Multiomics Machine Learning to Predict Neoadjuvant Chemotherapy Outcome and Relapse of Breast Cancer","volume":"7","author":"Wang","year":"2026","journal-title":"BME Front."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Liang, Y., Xu, H., Lin, J., Tang, W., Liu, X., Gan, K., Wan, Q., and Du, X. (2025). Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14407-2"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"e250760","DOI":"10.1148\/ryai.250760","article-title":"Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy","volume":"8","author":"Zhou","year":"2026","journal-title":"Radiol. Artif. Intell."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"e250789","DOI":"10.1148\/ryai.250789","article-title":"Tumor-preserving Deformable Registration of Longitudinal DCE Breast MRI during Neoadjuvant Chemotherapy","volume":"8","author":"Han","year":"2026","journal-title":"Radiol. Artif. Intell."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"101985","DOI":"10.1016\/j.tranon.2024.101985","article-title":"The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning","volume":"46","author":"Jiang","year":"2024","journal-title":"Transl. Oncol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"e243545","DOI":"10.1148\/radiol.243545","article-title":"Intratumoral Microbiome-related MRI Model for Predicting Breast Cancer Shrinkage Pattern Following Neoadjuvant Therapy","volume":"316","author":"Huang","year":"2025","journal-title":"Radiology"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1002\/ijc.35514","article-title":"MR imaging of breast cancer: Interpretable radiomics analysis to assess treatment response and survival prognosis after neoadjuvant therapy","volume":"157","author":"Hou","year":"2025","journal-title":"Int. J. Cancer"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"7852","DOI":"10.1002\/mp.16574","article-title":"A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies","volume":"50","author":"Saednia","year":"2023","journal-title":"Med. Phys."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/JBHI.2022.3216031","article-title":"Dual-Input Transformer: An End-to-End Model for Preoperative Assessment of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Ultrasonography","volume":"27","author":"Tong","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"5634","DOI":"10.1007\/s00330-023-09555-7","article-title":"Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer","volume":"33","author":"Yu","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ejca.2021.01.028","article-title":"Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer","volume":"147","author":"Jiang","year":"2021","journal-title":"Eur. J. Cancer"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1007\/s00259-023-06150-2","article-title":"Predictive value of 18F-FDG PET\/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: A multi-scanner\/center study with external validation","volume":"50","author":"Chen","year":"2023","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1007\/s00259-020-04684-3","article-title":"18F-FDG PET\/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients","volume":"47","author":"Li","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Liu, S., Du, S., Gao, S., Teng, Y., Jin, F., and Zhang, L. (2023). A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients. BMC Cancer, 23.","DOI":"10.1186\/s12885-022-10496-5"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1038\/s41698-024-00678-8","article-title":"Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer","volume":"8","author":"Guo","year":"2024","journal-title":"npj Precis. Oncol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"116222","DOI":"10.1016\/j.ejca.2026.116222","article-title":"Chemo-prAIdict Breast: A deep learning solution for predicting residual disease on biopsies of breast cancer patients treated with neoadjuvant chemotherapy","volume":"234","author":"Valderrama","year":"2026","journal-title":"Eur. J. Cancer"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1186\/s12967-022-03452-1","article-title":"Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets","volume":"20","author":"Zhang","year":"2022","journal-title":"J. Transl. Med."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1186\/s12967-025-06617-w","article-title":"Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer","volume":"23","author":"Nishizawa","year":"2025","journal-title":"J. Transl. Med."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"e70482","DOI":"10.1002\/cam4.70482","article-title":"Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model","volume":"13","author":"Comes","year":"2024","journal-title":"Cancer Med."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1056\/NEJMoa2203690","article-title":"Trastuzumab Deruxtecan in Previously Treated HER2-Low Advanced Breast Cancer","volume":"387","author":"Modi","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"116158","DOI":"10.1016\/j.ejca.2025.116158","article-title":"Prediction of HER2 changes post-neoadjuvant therapy based on fusion of ultrasound radiomics and clinicopathological features empowered by explainable AI: A multicenter study","volume":"232","author":"Yan","year":"2026","journal-title":"Eur. J. Cancer"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"e240312","DOI":"10.1148\/rycan.240312","article-title":"Radiomics-based Machine Learning Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Physiologically Decomposed Diffusion-weighted MRI","volume":"7","author":"Gilad","year":"2025","journal-title":"Radiol. Imaging Cancer"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Guo, L., Du, S., Gao, S., Zhao, R., Huang, G., Jin, F., Teng, Y., and Zhang, L. (2022). Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy. Cancers, 14.","DOI":"10.3390\/cancers14143515"},{"key":"ref_128","first-page":"568337","article-title":"Deep Learning Radiomics of Multiparametric MRI for Individualized Prediction of Axillary Lymph Node Response After Neoadjuvant Chemotherapy in Breast Cancer","volume":"18","author":"Li","year":"2026","journal-title":"Breast Cancer"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13058-020-01291-w","article-title":"A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy","volume":"22","author":"Sutton","year":"2020","journal-title":"Breast Cancer Res."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s13058-025-02035-4","article-title":"Biomechanical parameters quantified by MR elastography for predicting response to neoadjuvant chemotherapy and disease-free survival in breast cancer: A prospective longitudinal study","volume":"27","author":"Wang","year":"2025","journal-title":"Breast Cancer Res."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s40644-025-00929-2","article-title":"Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy","volume":"25","author":"Xu","year":"2025","journal-title":"Cancer Imaging"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1002\/jmri.70285","article-title":"MRI-Based Habitat Analysis for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study","volume":"63","author":"Zhu","year":"2026","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"7080","DOI":"10.1007\/s00330-024-10786-5","article-title":"Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: A multicenter study","volume":"34","author":"Fu","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"e187","DOI":"10.1093\/oncolo\/oyad227","article-title":"Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment","volume":"29","author":"Gu","year":"2024","journal-title":"Oncologist"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1097\/JS9.0000000000002105","article-title":"Elastography-based AI model can predict axillary status after neoadjuvant chemotherapy in breast cancer with nodal involvement: A prospective, multicenter, diagnostic study","volume":"111","author":"Huang","year":"2025","journal-title":"Int. J. Surg."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Liu, J., Leng, X., Yuan, Z., Li, Y., Luo, H., Chen, J., Li, R., Li, X., He, Z., and Zou, Q. (2025). Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models\u2014dual-center study. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-15148-y"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, J., Song, L., Zhao, H., Wan, X., and Peng, Y. (2025). Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: A clinical study. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14556-4"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"eadu1521","DOI":"10.1126\/sciadv.adu1521","article-title":"Multiomic integration reveals subtype-specific predictors of neoadjuvant treatment response in breast cancer","volume":"11","author":"Mo","year":"2025","journal-title":"Sci. Adv."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Rasic, D., Stovgaard, E.I.S., Jylling, A.M.B., Salgado, R., Hartman, J., Rantalainen, M., and L\u00e6nkholm, A.V. (2025). AI assessment of tumor-infiltrating lymphocytes on routine H&E-slides as a predictor of response to neoadjuvant therapy in breast cancer-a real-world study. Virchows Arch.","DOI":"10.1007\/s00428-025-04283-3"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"9690","DOI":"10.1038\/s41598-022-13917-4","article-title":"Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies","volume":"12","author":"Saednia","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Yue, J., Liu, J., Kang, X., Yuan, P., Wang, W., Wang, Z., Shang, C., Shang, Q., Li, G., and Dong, X. (2026). Development and validation of a pathomics-driven machine learning model for individualized prediction of neoadjuvant chemotherapy response and early recurrence in HR-positive, HER2-negative breast cancer. Front. Oncol., 16.","DOI":"10.3389\/fonc.2026.1770037"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11547-025-01958-4","article-title":"18F-FDG PET\/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients","volume":"130","author":"Urso","year":"2025","journal-title":"Radiol. Med."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"e1646","DOI":"10.1093\/oncolo\/oyae185","article-title":"18F-FDG PET\/CT for early prediction of pathological complete response in breast cancer neoadjuvant therapy: A retrospective analysis","volume":"29","author":"Wu","year":"2024","journal-title":"Oncologist"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s13058-024-01860-3","article-title":"Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients","volume":"26","author":"Yang","year":"2024","journal-title":"Breast Cancer Res."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Cheligeer, K., Wu, G., Laws, A., Quan, M.L., Li, A., Brisson, A.M., Xie, J., and Xu, Y. (2024). Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02677-y"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Fang, S., Xia, W., Zhang, H., Ni, C., Wu, J., Mo, Q., Jiang, M., Guan, D., Yuan, H., and Chen, W. (2024). A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1323226"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"e71221","DOI":"10.1002\/cam4.71221","article-title":"Interpretable Machine Learning for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using the Baseline Clinical and Pathological Characteristics","volume":"14","author":"Fang","year":"2025","journal-title":"Cancer Med."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13058-025-01960-8","article-title":"Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers","volume":"27","author":"Erber","year":"2025","journal-title":"Breast Cancer Res."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Qian, B., Yang, J., Zhou, J., Hu, L., Zhang, S., Ren, M., and Qu, X. (2022). Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.955250"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Qu, F., Luo, Y., Peng, Y., Yu, H., Sun, L., Liu, S., and Zeng, X. (2023). Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: A two-center study of 1,170 patients. Front. Immunol., 14.","DOI":"10.3389\/fimmu.2023.1335546"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"2344","DOI":"10.1038\/s41598-025-32167-8","article-title":"Development and validation of a nomogram incorporating YpT stage for predicting ypN0 using multicenter data in clinically node-positive breast cancer","volume":"16","author":"Shigematsu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"3731","DOI":"10.1097\/JS9.0000000000002391","article-title":"The development and validation of a risk stratification system for assessing axillary status after neoadjuvant therapy in node-positive breast cancer: A multicenter, prospective, observational study","volume":"111","author":"Huang","year":"2025","journal-title":"Int. J. Surg."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1200\/JCO.21.02439","article-title":"Intelligent Vacuum-Assisted Biopsy to Identify Breast Cancer Patients With Pathologic Complete Response (ypT0 and ypN0) After Neoadjuvant Systemic Treatment for Omission of Breast and Axillary Surgery","volume":"40","author":"Pfob","year":"2022","journal-title":"J. Clin. Oncol."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1097\/JS9.0000000000003999","article-title":"A foundation model for predicting outcomes of neoadjuvant chemotherapy in breast cancer","volume":"112","author":"Lin","year":"2026","journal-title":"Int. J. Surg."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"100662","DOI":"10.1016\/j.esmorw.2025.100662","article-title":"How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer","volume":"11","author":"Ruelle","year":"2026","journal-title":"ESMO Real World Data Digit. Oncol."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"S\u00e1ntha, D., Dobi, \u00c1., Iv\u00e1nyi, M., V\u00e1l\u00f3czi, D., Varga, Z., Ol\u00e1h, J., and Nikol\u00e9nyi, A. (2026). Efficacy and tolerability of scalp cooling in preventing alopecia during (neo)adjuvant chemotherapy for breast cancer. Pathol. Oncol. Res., 32.","DOI":"10.3389\/pore.2026.1612403"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/18\/2947\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T04:10:50Z","timestamp":1789272650000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/18\/2947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":156,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["cancers18182947"],"URL":"https:\/\/doi.org\/10.3390\/cancers18182947","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]}}}