The Journal of Practical Medicine >

2026 , Vol. 42 >Issue 9: 1491 - 1500

DOI: https://doi.org/10.3969/j.issn.1006-5725.2026.09.002

Feature Reports：Breast carcinoma

Advances in the application of multimodal ultrasound and artificial intelligence in the early diagnosis and prognostic prediction of breast cancer

  • Jiaqian ZHONG ,
  • Xiaoyan XIE ,
  • Yanling ZHENG
Expand
  • Department of Ultrasound Medicine，the First Affiliated Hospital of Sun Yat-sen University，Guangzhou 510080，Guangdong，China

Received date: 2025-12-01

  Online published: 2026-04-29

Fold

Abstract

With the rising incidence of breast cancer， early diagnosis and accurate prognostic evaluation have become pivotal for improving survival rates and optimizing personalized treatment. Multimodal ultrasound， by integrating technologies such as elastography， viscoelastic imaging， contrast-enhanced ultrasound， microflow imaging， and photoacoustic/ultrasound， can provide multi-dimensional information regarding lesion morphology， mechanical properties， blood perfusion， and tissue function in a non-invasive， dynamic， and real-time manner. This compensates for the limitations of conventional grayscale ultrasound in characterizing microvasculature， tissue stiffness， and biomechanical properties. Artificial Intelligence （AI）， particularly deep learning methods， has demonstrated remarkable capabilities in automatic feature extraction from images， multimodal data fusion， and predictive modeling， thereby providing technical support for transforming ultrasound imaging from image acquisition into clinical decision-making support. This paper systematically reviews recent advances in multimodal ultrasound technologies and their integration with AI in the areas of early breast cancer diagnosis， benign-malignant differentiation， prediction of lymph node metastasis， and prediction of molecular subtypes， summarizing both research achievements and existing limitations. Furthermore， it explores the application value of AI in lesion detection， automatic segmentation， radiomics feature fusion， and the construction of prognostic prediction models based on multi-source clinical and imaging data. Key challenges such as model interpretability， data standardization， cross-institution generalization capability， and clinical acceptability are analyzed， and strategic recommendations for promoting clinical translation are proposed. The synergistic development of multimodal ultrasound and AI holds promise for evolving ultrasound from an auxiliary detection tool into a pivotal component of early diagnosis， treatment， and comprehensive management throughout the breast cancer care continuum. However， its widespread application still requires concerted advancement in technological maturity， clinical validation， and policy support.

Key words： multimodal ultrasound; breast cancer; artificial intelligence; viscoelastic imaging; super-resolution ultrasound imaging

Cite this article

Jiaqian ZHONG , Xiaoyan XIE , Yanling ZHENG . Advances in the application of multimodal ultrasound and artificial intelligence in the early diagnosis and prognostic prediction of breast cancer[J]. The Journal of Practical Medicine, 2026 , 42(9) : 1491 -1500 . DOI: 10.3969/j.issn.1006-5725.2026.09.002

References

[1] KUBOTA K， NAKASHIMA K， NAKASHIMA K， et al. The Japanese breast cancer society clinical practice guidelines for breast cancer screening and diagnosis， 2022 edition［J］. Breast Cancer， 2024， 31（2）： 157-164. doi：10.1007/s12282-023-01521-x .
[2] GUNDRY K R. Breast ultrasound： Indications and findings［J］. Clin Obstet Gynecol， 2016， 59（2）： 380-393. doi：10.1097/GRF.0000000000000194 .
[3] KRATKIEWICZ K， PATTYN A， ALIJABBARI N， et al. Ultrasound and photoacoustic imaging of breast cancer： Clinical systems， challenges， and future outlook［J］. J Clin Med， 2022， 11（5）： 1165. doi：10.3390/jcm11051165 .
[4] GUO W， LI F， JIA C， et al. The clinical value of conventional ultrasound combined with contrast-enhanced ultrasound in the evaluation of BI-RADS 4 lesions detected by magnetic resonance imaging［J］. Br J Radiol， 2022， 95（1136）： 20220025. doi：10.1259/bjr.20220025 .
[5] SUN J， SHEN X， ZHANG N， et al. Combination of conventional ultrasound with quantitative and qualitative analyses of CEUS for the differentiation of benign and malignant breast solid lesions： A modified breast cancer model［J］. Asian J Surg， 2024： S1015-S9584（24）01844-X. doi：10.1016/j.asjsur.2024.08.104 .
[6] SUN J， ZHANG W， ZHAO Q， et al. Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions： Selection strategy for using different types of strain imaging in evaluation of breast lesions［J］. Eur J Radiol， 2024， 176： 111512. doi：10.1016/j.ejrad.2024.111512 .
[7] GOMES A F， JUSTINO D， TOMáS C， et al. Comparing the diagnostic performance of ultrasound elastography and magnetic resonance imaging to differentiate benign and malignant breast lesions： A systematic review and meta-analysis［J］. Acad Radiol， 2025， 32（8）： 4421-4434. doi：10.1016/j.acra.2024.12.061 .
[8] ZHOU J， ZHANG Y， SHI S. Ultrasound elastography： Advances and challenges in early detection of breast cancer［J］. Front Oncol， 2025， 15： 1589142. doi：10.3389/fonc.2025.1589142 .
[9] ZHANG Y， SUN X， LI J， et al. The diagnostic value of contrast-enhanced ultrasound and superb microvascular imaging in differentiating benign from malignant solid breast lesions： A systematic review and meta-analysis［J］. Clin Hemorheol Microcirc， 2022， 81（2）： 109-121. doi：10.3233/CH-211367 .
[10] COMBI C， AMICO B， BELLAZZI R， et al. A manifesto on explainability for artificial intelligence in medicine［J］. Artif Intell Med， 2022， 133： 102423. doi：10.1016/j.artmed.2022.102423 .
[11] S?FTOIU A， GILJA O H， SIDHU P S， et al. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Elastography in Non-Hepatic Applications： Update 2018［J］. Ultraschall Med， 2019， 40（4）：425-453. doi： 10.1055/a-0838-9937 .
[12] KUMAR V， DENIS M， GREGORY A， et al. Viscoelastic parameters as discriminators of breast masses： Initial human study results［J］. PLoS One， 2018， 13（10）： e0205717. doi：10.1371/journal.pone.0205717 .
[13] POUL S S， ORMACHEA J， GE G R， et al. Comprehensive experimental assessments of rheological models' performance in elastography of soft tissues［J］. Acta Biomater， 2022， 146： 259-273. doi：10.1016/j.actbio.2022.04.047 .
[14] JIA W， XIA S， JIA X， et al. Ultrasound viscosity imaging in breast lesions： A multicenter prospective study［J］. Acad Radiol， 2024， 31（9）： 3499-3510. doi：10.1016/j.acra.2024.03.017 .
[15] 刘钊，李慧，王敏敏，等.黏弹性成像联合剪切波弹性成像在乳腺癌诊断中的价值评估［J］.实用医学杂志，2025，41（18）：2806-2811.doi： 10.3969/j.issn.1006-5725.2025.18.004 .
[16] 朱彩霞.超声黏弹性成像技术对乳腺肿块良恶性鉴别的应用研究［D］.南昌：南昌大学，2025.doi：10.27232/d.cnki.gnchu.2025.000539 .
[17] 周锋盛， 袁琳， 浦浙宁， 等. 超声黏弹性成像瘤周参数对BI-RADS 4类乳腺结节良恶性的预测价值［J］. 南京医科大学学报（自然科学版）， 2025， 45（11）： 1563-1571. doi：10.7655/NYDXBNSN250876 .
[18] SU G H， XIAO Y， YOU C， et al. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets［J］. Sci Adv， 2023， 9（40）： eadf0837. doi：10.1126/sciadv.adf0837 .
[19] XU Y J， GONG H L， HU B， et al. Role of “Stiff Rim” sign obtained by shear wave elastography in diagnosis and guiding therapy of breast cancer［J］. Int J Med Sci， 2021， 18（15）： 3615-3623. doi：10.7150/ijms.64243 .
[20] BENE I， DONCI D D， GHERMAN D， et al. Vi-PLUS： Pioneering plane-wave ultrasound to assess breast glandular tissue in healthy women-a pilot study［J］. Cancers， 2025， 17（2）： 237. doi：10.3390/cancers17020237 .
[21] OPACIC T， DENCKS S， THEEK B， et al. Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization［J］. Nat Commun， 2018， 9（1）： 1527. doi：10.1038/s41467-018-03973-8 .
[22] SONG P， RUBIN J M， LOWERISON M R. Super-resolution ultrasound microvascular imaging： Is it ready for clinical use？［J］. Z Med Phys， 2023， 33（3）： 309-323. doi：10.1016/j.zemedi.2023.04.001 .
[23] DENCKS S， SCHMITZ G. Ultrasound localization microscopy［J］. Z Für Med Phys， 2023， 33（3）： 292-308. doi：10.1016/j.zemedi.2023.02.004 .
[24] PARK A Y， KWON M， WOO O H， et al. A prospective study on the value of ultrasound microflow assessment to distinguish malignant from benign solid breast masses： Association between ultrasound parameters and histologic microvessel densities［J］. Korean J Radiol， 2019， 20（5）： 759-772. doi：10.3348/kjr.2018.0515 .
[25] HUANG C， ZHANG W， GONG P， et al. Super-resolution ultrasound localization microscopy based on a high frame-rate clinical ultrasound scanner： An in-human feasibility study［J］. Phys Med Biol， 2021， 66（8）： 08NT01. doi：10.1088/1361-6560/abef45 .
[26] ZHANG G， LEI Y M， LI N， et al. Ultrasound super-resolution imaging for differential diagnosis of breast masses［J］. Front Oncol， 2022， 12： 1049991. doi：10.3389/fonc.2022.1049991 .
[27] XIA S， HUA Q， SONG Y， et al. Super-resolution ultrasound imaging of intranodal lymphatic sinuses for predicting sentinel lymph node metastasis in breast cancer： A preliminary study［J］. Eur Radiol， 2025， 35（10）： 6079-6088. doi：10.1007/s00330-025-11520-5 .
[28] FANG K， WANG L， HUANG H， et al. Construction of nucleolin-targeted lipid nanobubbles and contrast-enhanced ultrasound molecular imaging in triple-negative breast cancer［J］. Pharm Res， 2020， 37（7）： 145. doi：10.1007/s11095-020-02873-1 .
[29] DE KOEKKOEK-DOLL P K， ROBERTI S， VAN DEN BREKEL M W， et al. Value of assessing peripheral vascularization with micro-flow imaging， resistive index and absent hilum sign as predictor for malignancy in lymph nodes in head and neck squamous cell carcinoma［J］. Cancers， 2021， 13（20）： 5071. doi：10.3390/cancers13205071 .
[30] KANG T W， JEONG W K， KIM Y Y， et al. Comparison of super-resolution US and contrast material-enhanced US in detection of the spoke wheel sign in patients with focal nodular hyperplasia［J］. Radiology， 2021， 298（1）： 82-90. doi：10.1148/radiol.2020200885 .
[31] FORSBERG F， RO R J， FOX T B， et al. Contrast enhanced maximum intensity projection ultrasound imaging for assessing angiogenesis in murine glioma and breast tumor models： A comparative study［J］. Ultrasonics， 2011， 51（3）： 382-389. doi：10.1016/j.ultras.2010.11.004 .
[32] DU J， LI F H， FANG H， et al. Microvascular architecture of breast lesions： Evaluation with contrast-enhanced ultrasonographic micro flow imaging［J］. J Ultrasound Med， 2008， 27（6）： 833-842；quiz844. doi：10.7863/jum.2008.27.6.833 .
[33] EUN N L， LEE E， PARK A Y， et al. Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis［J］. Ultraschall Med， 2024， 45（4）： 412-417. doi：10.1055/a-2230-2455 .
[34] STEINBERG I， HULAND D M， VERMESH O， et al. Photoacoustic clinical imaging［J］. Photoacoustics， 2019， 14： 77-98. doi：10.1016/j.pacs.2019.05.001 .
[35] NEUSCHLER E I， BUTLER R， YOUNG C A， et al. A pivotal study of optoacoustic imaging to diagnose benign and malignant breast masses： A new evaluation tool for radiologists［J］. Radiology， 2018， 287（2）： 398-412. doi：10.1148/radiol.2017172228 .
[36] HUANG Z， TIAN H， LUO H， et al. Assessment of oxygen saturation in breast lesions using photoacoustic imaging： Correlation with benign and malignant disease［J］. Clin Breast Cancer， 2024， 24（4）： e210-e218.e1. doi：10.1016/j.clbc.2024.01.006 .
[37] FANG J S， GILLIES R D， GATENBY R A. Adaptation to hypoxia and acidosis in carcinogenesis and tumor progression［J］. Semin Cancer Biol， 2008， 18（5）： 330-337. doi：10.1016/j.semcancer.2008.03.011 .
[38] HUANG Z， MO S， LI G， et al. Prognosticating axillary lymph node metastasis in breast cancer through integrated photoacoustic imaging， ultrasound， and clinical parameters［J］. Breast Cancer Res， 2025， 27（1）： 123. doi：10.1186/s13058-025-02073-y .
[39] CHEN J， YIN Y， LI G， et al. Integrated nomogram to predict HER2 expression in breast tumor： Clinical， Ultrasound， and Photoacoustic imaging approaches［J］. Eur J Cancer， 2024， 209： 114259. doi：10.1016/j.ejca.2024.114259 .
[40] LI G， HUANG Z， TIAN H， et al. Deep learning combined with attention mechanisms to assist radiologists in enhancing breast cancer diagnosis： A study on photoacoustic imaging［J］. Biomed Opt Express， 2024， 15（8）： 4689-4704. doi：10.1364/BOE.530249 .
[41] FU Q， DONG H. Spiking neural network based on multi-scale saliency fusion for breast cancer detection［J］. Entropy， 2022， 24（11）： 1543. doi：10.3390/e24111543 .
[42] VALLEZ N， MATEOS-APARICIO-RUIZ I， RIENDA M A， et al. Comparative analysis of deep learning methods for breast ultrasound lesion detection and classification［J］. Phys Med， 2025， 134： 104993. doi：10.1016/j.ejmp.2025.104993 .
[43] LIU W， ANGUELOV D， ERHAN D， et al. SSD： Single shot MultiBox detector［C］//Computer Vision – ECCV 2016. Cham： Springer， 2016： 21-37. doi：10.1007/978-3-319-46448-0_2 .
[44] PUNN N S， AGARWAL S. RCA-IUnet： A residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging［J］. Mach Vis Appl， 2022， 33（2）： 27. doi：10.1007/s00138-022-01280-3 .
[45] ZHAI D， HU B， GONG X， et al. ASS-GAN： Asymmetric semi-supervised GAN for breast ultrasound image segmentation［J］. Neurocomputing， 2022， 493： 204-216. doi：10.1016/j.neucom.2022.04.021 .
[46] ILESANMI A E， CHAUMRATTANAKUL U， MAKHANOV S S. A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning［J］. Biocybern Biomed Eng， 2021， 41（2）： 802-818. doi：10.1016/j.bbe.2021.05.007 .
[47] RADHI E A， KAMIL M Y. Anisotropic diffusion method for speckle noise reduction in breast ultrasound images［J］. Inte J Intellig Engineer Syst， 2024， 17（2）. doi： 10.22266/ijies2024.0430.50 .
[48] BADAWY S M， MOHAMED A E A， HEFNAWY A A， et al. Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning-a feasibility study［J］. PLoS One， 2021， 16（5）： e0251899. doi：10.1371/journal.pone.0251899 .
[49] XUE C， ZHU L， FU H， et al. Global guidance network for breast lesion segmentation in ultrasound images［J］. Med Image Anal， 2021， 70： 101989. doi：10.1016/j.media.2021.101989 .
[50] PRAMANIK P， PRAMANIK R， SCHWENKER F， et al. DBU-Net： Dual branch U-Net for tumor segmentation in breast ultrasound images［J］. PLoS One， 2023， 18（11）： e0293615. doi：10.1371/journal.pone.0293615 .
[51] HUANG J， MAO Y， DENG J， et al. EMGANet： Edge-aware multi-scale group-mix attention network for breast cancer ultrasound image segmentation［J］. IEEE J Biomed Health Inform， 2025， 29（8）： 5631-5641. doi：10.1109/JBHI.2025.3546345 .
[52] SUN D， DONG C， YAN Y， et al. Challenge-aware U-Net for breast lesion segmentation in ultrasound images［J］. Pattern Recognit， 2025， 168： 111851. doi：10.1016/j.patcog.2025.111851 .
[53] HUANG J， HUANG J， ZHANG M， et al. UltraMamba： Mamba-based multimodal ultrasound image adaptive fusion for breast lesion segmentation［J］. IEEE Trans Med Imaging， 2026， PP： 1. doi：10.1109/TMI.2026.3653779 .
[54] KRIZHEVSKY A， SUTSKEVER I， HINTON G E. ImageNet classification with deep convolutional neural networks［J］. Commun ACM， 2017， 60（6）：84-90. doi： 10.1145/3065386 .
[55] TAGNAMAS J， RAMADAN H， YAHYAOUY A， et al. Correction： Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images［J］. Vis Comput Ind Biomed Art， 2024， 7（1）： 5. doi：10.1186/s42492-024-00156-9 .
[56] TAHERI F， RAHBAR K. Improving breast cancer classification in fine-grain ultrasound images through feature discrimination and a transfer learning approach［J］. Biomed Signal Process Control， 2025， 106： 107690. doi：10.1016/j.bspc.2025.107690 .
[57] CHEN S H， WU Y L， PAN C Y， et al. Breast ultrasound image classification and physiological assessment based on GoogLeNet［J］. J Radiat Res Appl Sci， 2023， 16（3）： 100628. doi：10.1016/j.jrras.2023.100628 .
[58] ZHAO M， YE N. High-dimensional ensemble learning classification： An ensemble learning classification algorithm based on high-dimensional feature space reconstruction［J］. Appl Sci， 2024， 14（5）：1956. doi：10.3390/app14051956 .
[59] LIU Y， LI J， ZHAO C， et al. FAMF-net： Feature alignment mutual attention fusion with region awareness for breast cancer diagnosis via imbalanced data［J］. IEEE Trans Med Imaging， 2025， 44（3）： 1153-1167. doi：10.1109/TMI.2024.3485612 .
[60] HOSSAIN S， AZAM S， MONTAHA S， et al. Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model［J］. Heliyon， 2023， 9（11）： e21369. doi：10.1016/j.heliyon.2023.e21369 .
[61] HE Q， YANG Q， SU H， et al. Multi-task learning for segmentation and classification of breast tumors from ultrasound images［J］. Comput Biol Med， 2024， 173： 108319. doi：10.1016/j.compbiomed.2024.108319 .
[62] AUMENTE-MAESTRO C， DíEZ J， REMESEIRO B. A multi-task framework for breast cancer segmentation and classification in ultrasound imaging［J］. Comput Methods Programs Biomed， 2025， 260： 108540. doi：10.1016/j.cmpb.2024.108540 .
[63] SU A， WANG X， XU H， et al. Multi-task learning for multi-scale breast cancer ultrasound image segmentation and classification based on visual perception［J］. Biomed Signal Process Control， 2025， 110： 108212. doi：10.1016/j.bspc.2025.108212 .
[64] ARUNKUMAR K E， WILSON M E， BLAKE N E， et al. Deep learning for tumor segmentation and multiclass classification in breast ultrasound images using pretrained models［J］. Sensors， 2025， 25（24）： 7557. doi：10.3390/s25247557 .
[65] NAKACH F Z， IDRI A， GOCERI E. A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification［J］. Artif Intell Rev， 2024， 57（12）： 327. doi：10.1007/s10462-024-10984-z .
[66] RANI S， AHMAD T， MASOOD S， et al. Diagnosis of breast cancer molecular subtypes using machine learning models on unimodal and multimodal datasets［J］. Neural Comput Appl， 2023， 35（34）： 24109-24121. doi：10.1007/s00521-023-09005-x .
[67] ASIF S， OU D， HADI F， et al. BreastUS-Net： An Attention-Guided Dual-Branch Network with Feature Fusion for Fine-Grained Breast Tumor Classification in Ultrasound Imaging［J］. IEEE J Biomed Health Inform， 2025. doi： 10.1109/JBHI.2025.3620256 .
[68] ASIF S， YAN Y， FENG B， et al. Improving Breast Cancer Diagnosis in Ultrasound Images Using Deep Learning with Feature Fusion and Attention Mechanism［J］. Acad Radiol， 2025， 32（9）：4997-5009. doi： 10.1016/j.acra.2025.05.007 .
[69] 刘丽， 谢月， 黄剑荣， 等. 人工智能乳腺超声诊断系统在乳腺癌筛查中的应用价值［J］. 影像研究与医学应用， 2025， 9（22）： 111-113. doi：10.20267/j.issn.2096-3807.2025.22.035 .
[70] XIANG H， WANG X， XU M， et al. Deep Learning-assisted Diagnosis of Breast Lesions on US Images： A Multivendor， Multicenter Study［J］. Radiol Artif Intell， 2023， 5（5）：e220185. doi： 10.1148/ryai.220185 .
[71] AZHAR K， LEE B D， BYON S S， et al. AI-powered synthesis of structured multimodal breast ultrasound reports integrating radiologist annotations and deep learning analysis［J］. Bioengineering， 2024， 11（9）： 890. doi：10.3390/bioengineering11090890 .
[72] LIAO J， GUI Y， LI Z， et al. Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population： A retrospective， multicentre， cohort study［J］. EClinicalMedicine， 2023， 60： 102001. doi：10.1016/j.eclinm.2023.102001 .
[73] QIAN L， LIU X， ZHOU S， et al. A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years［J］. Front Endocrinol， 2024， 15： 1323452. doi：10.3389/fendo.2024.1323452 .
[74] ZHENG X， YAO Z， HUANG Y， et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer［J］. Nat Commun， 2020， 11（1）：1236. doi： 10.1038/s41467-020-15027-z .
[75] GONG C， WU Y， ZHANG G， et al. Computer-assisted diagnosis for axillary lymph node metastasis of early breast cancer based on transformer with dual-modal adaptive mid-term fusion using ultrasound elastography［J］. Comput Med Imaging Graph， 2025， 119：102472. doi： 10.1016/j.compmedimag.2024.102472 .
[76] AGYEKUM E A， KONG W， AGYEKUM D N， et al. Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study［J］. Sci Rep， 2025， 15（1）：27796. doi： 10.1038/s41598-025-13086-0 .
[77] GAO Y， GU D， LI J， et al. Preoperative assessment of axillary lymph node tumor burden in cT1-2N0 breast cancer patients with a modality-adaptive network based on sentinel lymph node ultrasound images［J］. Sci Rep， 2026， 16： 4228. doi：10.1038/s41598-025-34371-y .
[78] FENG X， SHI Y， WU M， et al. Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model［J］. Breast Cancer Res， 2025， 27（1）：30. doi： 10.1186/s13058-025-01971-5 .
[79] GUO J， CHEN B， CAO H， et al. Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer［J］. NPJ Precis Oncol， 2024， 8（1）：189. doi： 10.1038/s41698-024-00678-8 .
[80] FU Y， LEI Y T， HUANG Y H， et al. Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer： A multicenter study［J］. Eur Radiol， 2024， 34（11）：7080-7089. doi： 10.1007/s00330-024-10786-5 .
[81] LIU Y， WANG Y， HUANG J， et al. Deep learning-based prediction of axillary pathological complete response in patients with breast cancer using longitudinal multiregional ultrasound［J］. EBioMedicine， 2025， 119：105896. doi： 10.1016/j.ebiom.2025.105896 .
[82] LI M， XU P， HU J， et al. From challenges and pitfalls to recommendations and opportunities： Implementing federated learning in healthcare［J］. Med Image Anal， 2025， 101： 103497. doi：10.1016/j.media.2025.103497 .
Options
Outlines

/

〈 〉