{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T12:48:45Z","timestamp":1792154925719,"version":"4.4.0"},"reference-count":50,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>The significant sonographic overlap between metastatic and ultrasound-atypical reactive hyperplastic lymph nodes remains a challenge in subjective, experience-based ultrasound diagnosis. Consequently, clinicians rely on biopsy, which is an invasive procedure associated with complications such as bleeding and infection, as well as the risk of false-negatives that exacerbate patient anxiety.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>In this multicenter retrospective study, patients with suspicious lymph nodes from two institutions were enrolled. Conventional and habitat-based radiomic features, capturing intratumoral heterogeneity, were extracted from ultrasound images. After feature selection via ElasticNet regression and multicollinearity removal, four machine learning models were developed. Hyperparameters were optimized using fivefold cross-validation. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA), and the model was interpreted using SHapley Additive exPlanation (SHAP) analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>A total of 1,230 patients (training set: 702; internal set: 302; external set: 226) were included in this study. Eight independent risk factors (e.g., age, long-to-short axis ratio, and cortical morphology) were identified. The random forest fusion model achieved an area under the curve (AUC) of 0.910 and an F1 score of 0.806 in the external set, significantly surpassing the clinical model (AUC = 0.690). Compared with conventional radiomics, the fusion model showed superior reclassification (net reclassification improvement = 0.562, integrated discrimination improvement = 0.144). SHAP analysis linked malignancy risk to higher gray-level nonuniformity and lower elongation, ensuring clinical plausibility. DCA confirmed robust clinical net benefit across all cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The fusion model, integrating radiomic and habitat features, enables noninvasive suspicious lymph node prediction. It may reduce unnecessary biopsies in low-risk patients and provide incremental value for individualized preoperative management by quantifying spatial characteristics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jcrt.jcrt_445_26","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T08:00:08Z","timestamp":1787817608000},"page":"688-701","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Accurate differentiation of metastatic and ultrasound-atypical reactive hyperplastic lymph nodes using a fusion model integrating ultrasound radiomics and habitatomics: A multicenter study"],"prefix":"10.4103","volume":"22","author":[{"given":"Jiafei","family":"Shen","sequence":"first","affiliation":[{"name":"Graduate School, Zhejiang Chinese Medical University, Hangzhou 310053, China"},{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyan","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"},{"name":"Wenling Institute of Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jincao","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"},{"name":"Wenling Institute of Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang, China"},{"name":"Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, 310022, China"},{"name":"Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wenling Institute of Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang, China"},{"name":"Center of Intelligent Diagnosis and Therapy (Taizhou), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Taizhou, Zhejiang, China"},{"name":"Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lujiao","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, 510060, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"},{"name":"Wenling Institute of Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang, China"},{"name":"Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, 310022, China"},{"name":"Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China"},{"name":"Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"R1-20261016","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1146\/annurev-physiol-022724-104908","article-title":"Transport and immune functions of the lymphatic system","volume":"87","author":"Davis","year":"2025","journal-title":"Annu Rev Physiol"},{"key":"R2-20261016","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.1039\/D2RA07414A","article-title":"Albumin-based nanoparticle for dual-modality imaging of the lymphatic system","volume":"13","author":"Li","year":"2023","journal-title":"RSC Adv"},{"key":"R3-20261016","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10585-021-10123-w","article-title":"The lymphatic system and sentinel lymph nodes: Conduit for cancer metastasis","volume":"39","author":"Leong","year":"2022","journal-title":"Clin Exp Metastasis"},{"key":"R4-20261016","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.trecan.2025.04.008","article-title":"Tumor-draining lymph nodes-friend or foe during immune checkpoint therapy?","volume":"11","author":"von Renesse","year":"2025","journal-title":"Trends Cancer"},{"key":"R5-20261016","doi-asserted-by":"crossref","first-page":"100121","DOI":"10.1016\/j.engmed.2026.100121","article-title":"Innate lymphoid cells as emerging players in cancer immunotherapy","volume":"3","author":"Kong","year":"2026","journal-title":"EngMedicine"},{"key":"R6-20261016","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1002\/uog.23617","article-title":"Terms, definitions and measurements to describe sonographic features of lymph nodes: Consensus opinion from the Vulvar International Tumor Analysis (VITA) group","volume":"57","author":"Fischerova","year":"2021","journal-title":"Ultrasound Obstet Gynecol"},{"key":"R7-20261016","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1016\/j.ultrasmedbio.2025.08.010","article-title":"An interpretable radiomics model integrating ultrasound and clinical features for multiclass classification of axillary lymph nodes","volume":"51","author":"Zheng","year":"2025","journal-title":"Ultrasound Med Biol"},{"key":"R8-20261016","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1001\/jamaoto.2024.2686","article-title":"Enlarged lymph nodes","volume":"150","author":"Stoler","year":"2024","journal-title":"JAMA Otolaryngol Head Neck Surg"},{"key":"R9-20261016","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.1021\/acscentsci.5c00509","article-title":"Reshaping tumor-lymph node immune axis via targeted lymphatic delivery of dual-functional immune modulator for enhanced cancer immunotherapy","volume":"11","author":"Lim","year":"2025","journal-title":"ACS Cent Sci"},{"key":"R10-20261016","doi-asserted-by":"crossref","first-page":"eabg3551","DOI":"10.1126\/sciimmunol.abg3551","article-title":"Tumor-draining lymph nodes: At the crossroads of metastasis and immunity","volume":"6","author":"du Bois","year":"2021","journal-title":"Sci Immunol"},{"key":"R11-20261016","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1109\/JBHI.2024.3515995","article-title":"LymoNet: An advanced neck lymph node detection network for ultrasound images","volume":"29","author":"Tan","year":"2025","journal-title":"IEEE J Biomed Health Inform"},{"key":"R12-20261016","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2024.3485612","article-title":"FAMF-Net: Feature alignment mutual attention fusion with region awareness for breast cancer diagnosis via imbalanced data","volume":"44","author":"Liu","year":"2025","journal-title":"IEEE Trans Med Imaging"},{"key":"R13-20261016","doi-asserted-by":"crossref","first-page":"e179","DOI":"10.1016\/j.clml.2020.09.012","article-title":"Use of ultrasonography facilitates noninvasive evaluation of lymphadenopathy in a lymph node diagnostic clinic","volume":"21","author":"Loh","year":"2021","journal-title":"Clin Lymphoma Myeloma Leuk"},{"key":"R14-20261016","doi-asserted-by":"crossref","first-page":"e232554","DOI":"10.1148\/radiol.232554","article-title":"Combining radiomics and autoencoders to distinguish benign and malignant breast tumors on US images","volume":"312","author":"Magnuska","year":"2024","journal-title":"Radiology"},{"key":"R15-20261016","doi-asserted-by":"crossref","first-page":"316","DOI":"10.11152\/mu-4420","article-title":"Contribution of MicroFlow imaging in the diagnosis of cystic artery pseudoaneurysm including weak turbulent flow","volume":"26","author":"Akin","year":"2024","journal-title":"Med Ultrason"},{"key":"R16-20261016","doi-asserted-by":"crossref","first-page":"261","DOI":"10.3233\/CH-231876","article-title":"A combination of ultrasound and contrast-enhanced ultrasound improves diagnostic accuracy for the differentiation of cervical tuberculous lymphadenitis from primary lymphoma","volume":"85","author":"Liu","year":"2023","journal-title":"Clin Hemorheol Microcirc"},{"key":"R17-20261016","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.3390\/cancers17122030","article-title":"Lymph node reporting and data system (LN-RADS)-retrospective evaluation for ultrasound classification of superficial lymph nodes","volume":"17","author":"Chudobi\u0144ski","year":"2025","journal-title":"Cancers (Basel)"},{"key":"R18-20261016","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/pcmedi\/pbaa003","article-title":"Benign lymph node microenvironment is associated with response to immunotherapy","volume":"3","author":"Toki","year":"2020","journal-title":"Precis Clin Med"},{"key":"R19-20261016","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1186\/s13058-025-02138-y","article-title":"Deep learning-powered multi-parametric ultrasound for classifying metastatic versus reactive axillary lymph nodes","volume":"27","author":"Saini","year":"2025","journal-title":"Breast Cancer Res"},{"key":"R20-20261016","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1148\/radiol.211101","article-title":"Ultrasonography of superficial soft-tissue masses: Society of radiologists in ultrasound consensus conference statement","volume":"304","author":"Jacobson","year":"2022","journal-title":"Radiology"},{"key":"R21-20261016","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1055\/a-0838-9937","article-title":"The EFSUMB guidelines and recommendations for the clinical practice of elastography in non-hepatic applications: Update 2018","volume":"40","author":"S\u0103ftoiu","year":"2019","journal-title":"Ultraschall Med"},{"key":"R22-20261016","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.acra.2024.08.024","article-title":"US-based radiomics analysis of different machine learning models for differentiating benign and malignant BI-RADS 4A breast lesions","volume":"32","author":"Ye","year":"2025","journal-title":"Acad Radiol"},{"key":"R23-20261016","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1001\/jamaoto.2025.2850","article-title":"Percutaneous ultrasound-guided biopsy for sampling neck lesions","volume":"151","author":"Satani","year":"2025","journal-title":"JAMA Otolaryngol Head Neck Surg"},{"key":"R24-20261016","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1016\/j.acra.2022.10.015","article-title":"Comparison of traditional radiomics, deep learning radiomics and fusion methods for axillary lymph node metastasis prediction in breast cancer","volume":"30","author":"Li","year":"2023","journal-title":"Acad Radiol"},{"key":"R25-20261016","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1186\/s13058-024-01909-3","article-title":"Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: A multicenter study","volume":"26","author":"Xiong","year":"2024","journal-title":"Breast Cancer Res"},{"key":"R26-20261016","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s11547-023-01739-x","article-title":"Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer","volume":"129","author":"Xu","year":"2024","journal-title":"Radiol Med"},{"key":"R27-20261016","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.4103\/jcrt.jcrt_493_25","article-title":"Diagnostic performance of the deep learning method trained using MRI and F-18 FDG-PET\/CT images in the evaluation of axillary lymph node metastasis in breast cancer patients","volume":"21","author":"Aydede","year":"2025","journal-title":"J Cancer Res Ther"},{"key":"R28-20261016","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.4103\/jcrt.jcrt_712_25","article-title":"CT-based delta-radiomics nomogram for predicting post-neoadjuvant chemoradiotherapy lymph node metastasis in esophageal squamous cell carcinoma: A feasibility study","volume":"21","author":"Zhang","year":"2025","journal-title":"J Cancer Res Ther"},{"key":"R29-20261016","doi-asserted-by":"crossref","first-page":"731","DOI":"10.2147\/CMAR.S511796","article-title":"Habitat analysis in tumor imaging: advancing precision medicine through radiomic subregion segmentation","volume":"17","author":"Wu","year":"2025","journal-title":"Cancer Manag Res"},{"key":"R30-20261016","doi-asserted-by":"crossref","first-page":"e232047","DOI":"10.1148\/radiol.232047","article-title":"Decoding intratumoral heterogeneity: Clinical potential of habitat imaging based on radiomics","volume":"309","author":"Zhang","year":"2023","journal-title":"Radiology"},{"key":"R31-20261016","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1002\/jum.70129","article-title":"Ultrasound habitat radiomics plus clinical features for pre-operative prediction of sentinel lymph node metastasis in breast cancer","volume":"45","author":"Wang","year":"2026","journal-title":"J Ultrasound Med"},{"key":"R32-20261016","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1186\/s12967-025-06474-7","article-title":"Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer","volume":"23","author":"Chen","year":"2025","journal-title":"J Transl Med"},{"key":"R33-20261016","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s41392-023-01576-4","article-title":"Lymph node metastasis in cancer progression: Molecular mechanisms, clinical significance and therapeutic interventions","volume":"8","author":"Ji","year":"2023","journal-title":"Signal Transduct Target Ther"},{"key":"R34-20261016","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1080\/17425247.2018.1446937","article-title":"Lymph node metastases: Importance of detection and treatment strategies","volume":"15","author":"Obinu","year":"2018","journal-title":"Expert Opin Drug Deliv"},{"key":"R35-20261016","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1111\/1346-8138.16585","article-title":"Complications and risk factors of punch biopsy: A retrospective large-scale study","volume":"50","author":"Yasui","year":"2023","journal-title":"J Dermatol"},{"key":"R36-20261016","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s42492-023-00147-2","article-title":"Application and prospects of AI-based radiomics in ultrasound diagnosis","volume":"6","author":"Zhang","year":"2023","journal-title":"Vis Comput Ind Biomed Art"},{"key":"R37-20261016","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1002\/jum.16483","article-title":"Machine learning model for predicting axillary lymph node metastasis in clinically node positive breast cancer based on peritumoral ultrasound radiomics and SHAP feature analysis","volume":"43","author":"Wang","year":"2024","journal-title":"J Ultrasound Med"},{"key":"R38-20261016","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1177\/13860291241304060","article-title":"To predict cervical lymph node tuberculosis based on clinical-multimodal ultrasound radiomics model","volume":"89","author":"Wang","year":"2025","journal-title":"Clin Hemorheol Microcirc"},{"key":"R39-20261016","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1007\/s00330-022-09122-6","article-title":"Using ultrasound radiomics analysis to diagnose cervical lymph node metastasis in patients with nasopharyngeal carcinoma","volume":"33","author":"Lin","year":"2023","journal-title":"Eur Radiol"},{"key":"R40-20261016","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s00330-020-07141-9","article-title":"Radiomics in predicting treatment response in non-small-cell lung cancer: Current status, challenges and future perspectives","volume":"31","author":"Chetan","year":"2021","journal-title":"Eur Radiol"},{"key":"R41-20261016","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1007\/s00330-020-07562-6","article-title":"Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics","volume":"31","author":"Mao","year":"2021","journal-title":"Eur Radiol"},{"key":"R42-20261016","doi-asserted-by":"crossref","first-page":"103018","DOI":"10.1016\/j.ebiom.2020.103018","article-title":"Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer","volume":"60","author":"Guo","year":"2020","journal-title":"EBioMedicine"},{"key":"R43-20261016","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1186\/s12885-023-11498-7","article-title":"Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: A retrospective study","volume":"23","author":"Li","year":"2023","journal-title":"BMC Cancer"},{"key":"R44-20261016","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s12885-025-13447-y","article-title":"Artificial intelligence performance in ultrasound-based lymph node diagnosis: A systematic review and meta-analysis","volume":"25","author":"Han","year":"2025","journal-title":"BMC Cancer"},{"key":"R45-20261016","doi-asserted-by":"crossref","first-page":"105250","DOI":"10.1016\/j.compbiomed.2022.105250","article-title":"Deep learning prediction of axillary lymph node status using ultrasound images","volume":"143","author":"Sun","year":"2022","journal-title":"Comput Biol Med"},{"key":"R46-20261016","doi-asserted-by":"crossref","first-page":"2395061","DOI":"10.1080\/07853890.2024.2395061","article-title":"Utilizing multiclassifier radiomics analysis of ultrasound to predict high axillary lymph node tumour burden in node-positive breast cancer patients: A multicentre study","volume":"56","author":"Wu","year":"2024","journal-title":"Ann Med"},{"key":"R47-20261016","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1186\/s12916-022-02469-z","article-title":"Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy","volume":"20","author":"Zhu","year":"2022","journal-title":"BMC Med"},{"key":"R48-20261016","doi-asserted-by":"crossref","first-page":"8211","DOI":"10.21037\/qims-24-558","article-title":"Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: A retrospective study","volume":"14","author":"Wu","year":"2024","journal-title":"Quant Imaging Med Surg"},{"key":"R49-20261016","doi-asserted-by":"crossref","first-page":"17085","DOI":"10.1038\/s41598-025-02181-x","article-title":"Habitat-based radiomics from contrast-enhanced CT and clinical data to predict lymph node metastasis in clinical N0 peripheral lung adenocarcinoma <\/=3 Cm","volume":"15","author":"Huang","year":"2025","journal-title":"Sci Rep"},{"key":"R50-20261016","doi-asserted-by":"crossref","first-page":"2463","DOI":"10.1007\/s00405-022-07802-z","article-title":"Ultrasonographic and cytological characterization of ultrasound-guided fine-needle aspiration cytology of cervical lymph nodes for false-negative and false-positive diagnosis","volume":"280","author":"Ren","year":"2023","journal-title":"Eur Arch Otorhinolaryngol"}],"container-title":["Journal of Cancer Research and Therapeutics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jcrt.jcrt_445_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T12:01:34Z","timestamp":1792152094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jcrt.jcrt_445_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,26]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.4103\/jcrt.jcrt_445_26","relation":{},"ISSN":["1998-4138","0973-1482"],"issn-type":[{"value":"1998-4138","type":"print"},{"value":"0973-1482","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,26]]},"assertion":[{"value":"2026-03-19","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-08","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}