Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.

Journal: Academic radiology
PMID: 39542804

Abstract

RATIONALE AND OBJECTIVES: The early prediction of response to neoadjuvant chemotherapy (NAC) will aid in the development of personalized treatments for patients with breast cancer. This study investigated the value of longitudinal multimodal deep learning (DL) based on breast MR and ultrasound (US) in predicting pathological complete response (pCR) after NAC.

Authors

  • Qiao Zeng
    Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China.
  • Lan Liu
    School of Statistics, University of Minnesota at Twin Cities.
  • Chongwu He
    Department of Breast Surgery, Jiangxi Cancer Hospital＆Institute，Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang 330029, Jiangxi Province, China (C.H., X.Z., T.Y.).
  • Xiaoqiang Zeng
    Department of Breast Surgery, Jiangxi Cancer Hospital＆Institute，Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang 330029, Jiangxi Province, China (C.H., X.Z., T.Y.).
  • Pengfei Wei
    School of Computing, National University of Singapore, Singapore. Electronic address: dcsweip@nus.edu.sg.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Ning Mao
    Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, People's Republic of China.
  • Tenghua Yu
    Department of Breast Surgery, Jiangxi Cancer Hospital＆Institute，Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang 330029, Jiangxi Province, China (C.H., X.Z., T.Y.). Electronic address: tengyeyu2024@163.com.

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