Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges_Journal of Biomedical Engineering

Authors：

HE Sunwei ¹ , LI Xiujuan ² , XIE Yuanzhong ² , HOU Jixue ³ , HAN Baosan ⁴ ,  NIE Shengdong ¹

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;

Corresponding author：

Keywords：

Breast cancer; Neoadjuvant chemotherapy; Pathological complete response; Deep learning; Treatment response prediction

DOI：

10.7507/1001-5515.202503075

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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.

Citation： HE Sunwei, LI Xiujuan, XIE Yuanzhong, HOU Jixue, HAN Baosan, NIE Shengdong. Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges. Journal of Biomedical Engineering, 2025, 42(5): 1076-1084. doi: 10.7507/1001-5515.202503075 Copy

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Journal of Biomedical Engineering

Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges

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