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Daqu Zhang, picture taken outdoors. Photo.

Daqu Zhang

Doctoral student

Daqu Zhang, picture taken outdoors. Photo.

Contact details

Email: daqu [dot] zhang [at] mgeo [dot] lu [dot] se
Phone: +46 72 446 12 35

Organisation

Department of Earth and Environmental Sciences (MGeo)
Service point: 16

Webpage

Daqu Zhangs profile in Lund University research portal

Other affiliations

Member of research environment
LUCC: Lund University Cancer Centre
Doctoral student
Computational Science for Health and Environment
Doctoral student
Breast cancer treatment
Doctoral student
Centre for Environmental and Climate Science (CEC)
Research project participant
The Liquid Biopsy and Tumor Progression in Breast Cancer

Publications

Displaying of publications. Sorted by year, then title.

Multimodal Gene Expression Deep Learning for Predicting Sentinel Lymph Node Macro-metastasis in Early Breast Cancer: Development and Validation in the SCAN-B Cohort
Daqu Zhang, Johan Staaf, Pär-Ola Bendahl, Looket Dihge, Mattias Ohlsson, et al.
(2026)
Preprint
Preoperative Nodal Metastatic Risk Evaluation in Early Breast Cancer : A Multimodal Deep Learning Approach
Daqu Zhang
(2026)
Dissertation
Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis
Cornelia Rejmer, Malin Hjärtström, Pär-Ola Bendahl, Looket Dihge, Ida Skarping, et al.
(2025) Systematic Reviews, 14
Journal article
Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer
Daqu Zhang, Looket Dihge, Pär-Ola Bendahl, Ida Arvidsson, Magnus Dustler, et al.
(2025) npj Digital Medicine, 8
Journal article
Enhancing the Prediction of Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Routine Full-Breast Mammograms
Daqu Zhang, Looket Dihge, Pär-Ola Bendahl, Ida Arvidsson, Magnus Dustler, et al.
(2025) Breast Cancer Research
Preprint
Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics
Daqu Zhang, Miriam Svensson, Patrik Edén, Looket Dihge
(2024) Scientific Reports, 14
Journal article

Last updated: 18 Dec 2025 | camilla [dot] king [at] cec [dot] lu [dot] se