Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges.
10.7507/1001-5515.202503075
- Author:
Sunwei HE
1
;
Xiujuan LI
2
;
Yuanzhong XIE
2
;
Jixue HOU
3
;
Baosan HAN
4
;
Shengdong NIE
1
Author Information
1. Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
2. Medical Imaging Center, Tai'an Central Hospital Affiliated with Qingdao University, Tai'an, Shandong 271000, P. R. China.
3. Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang 832000, P. R. China.
4. Department of Breast Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, P. R. China.
- Publication Type:English Abstract
- Keywords:
Breast cancer;
Deep learning;
Neoadjuvant chemotherapy;
Pathological complete response;
Treatment response prediction
- MeSH:
Humans;
Breast Neoplasms/pathology*;
Neoadjuvant Therapy;
Artificial Intelligence;
Female;
Machine Learning;
Deep Learning;
Chemotherapy, Adjuvant;
Treatment Outcome
- From:
Journal of Biomedical Engineering
2025;42(5):1076-1084
- CountryChina
- Language:Chinese
-
Abstract:
With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.
