Improving HER2-Positive Breast Cancer Targeted Therapy Prediction Using multiMSnet: A Multi-Scale Pathological Image-Based Approach-Reference-Cited by-同舟云学术

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Improving HER2-Positive Breast Cancer Targeted Therapy Prediction Using multiMSnet: A Multi-Scale Pathological Image-Based Approach

Published:2023-12-04 Issue: Volume: Page:304-310
ISSN:
Container-title:2023 IEEE International Conference on Data Mining Workshops (ICDMW)
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Author:

Liu Xiaohua¹,Li Yi²,Xiong Xiaomin²,Wu Yihan²,Xu Mengke²,Chen Lin³,Lin Bo⁴,Xu Bo⁵,Liu Guoxiang¹

Affiliation:

1. Chongqing University,Bioengineering College,Chongqing,China

2. Chongqing University,School of Medicine,Chongqing,China

3. Chinese Academy of Sciences,Chongqing Institute of Green and Intelligent Technology,Chongqing,China

4. Chongqing University Cancer Hospital Chongqing University,Chongqing,China

5. Chongqing University Cancer Hospital,Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer,Chongqing,China

Funder

National Natural Science Foundation of China

Chongqing University

Health

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10411504/10411508/10411594.pdf?arnumber=10411594

Reference24 articles.

1. Evolving standards of care and new challenges in the management of HER2‐positive breast cancer

2. Multicenter study of the clinicopathological features and recurrence risk prediction model of early-stage breast cancer with low-positive human epidermal growth factor receptor 2 expression in China (Chinese Society of Breast Surgery 021)

3. Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm

4. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

5. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study

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