Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-06-009 ISMRM Abstract

Predicting Non-Sentinel Lymph Node Metastasis with a Swin-Transformer-Based Deep Learning Model Using DCE-MRI

Primary: Body - Breast
Secondary: Analysis Methods - Radiomics
560-06-009 · AI Image Processing · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row A
Accepted
Yi Dai 1, Ning Mao2
1Peking University Shenzhen Hospital, Shenzhen, China
2Yantai Yuhuangding Hospital, Yantai, China
Presenting Author: Yi Dai

Synopsis

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References

1. de Boniface J, Filtenborg Tvedskov T, Rydén L, Szulkin R, Reimer T, Kühn T, Kontos M, Gentilini OD, Olofsson Bagge R, Sund M, Lundstedt D, Appelgren M, Ahlgren J, Norenstedt S, Celebioglu F, Sackey H, Scheel Andersen I, Hoyer U, Nyman PF, Vikhe Patil E, Wieslander E, Dahl Nissen H, Alkner S, Andersson Y, Offersen BV, Bergkvist L, Frisell J, Christiansen P; SENOMAC Trialists’ Group; SENOMAC Trialists' Group. Omitting Axillary Dissection in Breast Cancer with Sentinel-Node Metastases. N Engl J Med. 2024 Apr 4;390(13):1163-1175. doi: 10.1056/NEJMoa2313487. PMID: 38598571. [doi] [pmid]
2. Chen M, Kong C, Lin G, Chen W, Guo X, Chen Y, Cheng X, Chen M, Shi C, Xu M, Sun J, Lu C, Ji J. Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study. EClinicalMedicine. 2023 Aug 24;63:102176. doi: 10.1016/j.eclinm.2023.102176. PMID: 37662514; PMCID: PMC10474371. eClinicalMedicine, Volume 63, 102176 [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-06-009