Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer.

Journal: Academic radiology

PMID: 38641451

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP-L) grades 1-2 or high MP (MP-H) grades 3-5) in patients with ER-positive/HER2-negative breast cancer.

Authors

Dong Xing
Yongbin Lv

Department of Radiology,Yantai Yuhuangding Hospital, Yantai, Shandong 264000 China.
Bolin Sun

Department of Interventional Therapy, Yantai Yuhuangding Hospital, Yantai, Shandong 264000, China.
Tongpeng Chu

Department of Radiology,Yantai Yuhuangding Hospital, Yantai, Shandong 264000 China; Big Data and Artificial Intelligence Lab, Yantai Yuhuangding Hospital, Yantai, Shandong 264000, China.
Qianhao Bao

Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250300, China.
Han Zhang

Johns Hopkins University, Baltimore, MD, USA.

Keywords

Adult Aged Breast Neoplasms Chemotherapy, Adjuvant Contrast Media Deep Learning Female Humans Mammography Middle Aged Neoadjuvant Therapy Nomograms Receptor, ErbB-2 Receptors, Estrogen Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38641451)

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer.

Abstract

Authors

Keywords

External Resources

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