Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Sep 17, 2025

Abstract

PURPOSE: This study aims to evaluate the performance of artificial intelligence (AI)-assisted PET imaging in predicting neoadjuvant chemotherapy (NAC) response in breast cancer patients. METHODS: The Ovid MEDLINE, Ovid Embase, Cochrane, Web of Science, and IEEE Xplore databases were systematically searched for studies utilizing AI algorithms in PET imaging for predicting responses to NAC in breast cancer, covering publications up to June 26, 2025. Binary diagnostic accuracy data were extracted for meta-analysis, with the area under the curve (AUC) serving as the primary outcome. Subgroup analyses and meta-regression analyses were conducted to explore potential sources of heterogeneity. RESULTS: Eighteen studies were eligible for systematic review, and eleven studies that selected 907 patients were included in the meta-analysis, with a pooled AUC of 0.80 (95% confidence interval [CI]: 0.77-0.84). However, significant heterogeneity was observed among the studies, with a I² of 79.65% (95% CI: 74.69-84.60) for sensitivity and 86.62% (95% CI: 83.73-89.51) for specificity. Meta-regression analyses revealed that patient sample size and the integration of clinical data in the models were significant contributors to heterogeneity. CONCLUSIONS: The use of AI in predicting treatment response to NAC in breast cancer based on PET imaging demonstrated promising accuracy and potential for clinical use. But its clinical implementation is challenged by methodological variability, small datasets, lack of external validation and limited interpretability. Future research should prioritize the improvement of dataset quality and the integration of explainable AI (XAI) to facilitate the broader adoption of AI in clinical practice.

Authors

  • Yuhan Chen
    Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
  • Yuan Sun
    Pharmacy Department of Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
  • Yuanjie Chen
    Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China.
  • Jucheng Zhang
    Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310019, People's Republic of China.
  • Hang Zhang
    Department of Cardiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • La Dong
    Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China.
  • Xiaohui Zhang
    Department of Orthopaedic Surgery, the Second Hospital &Clinical Medical School, Lanzhou University, Lanzhou, Gansu Province, China.
  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Yan Zhong
  • Mei Tian
    Huashan Hospital and Human Phenome Institute, Fudan University, Shanghai, China; [email protected] [email protected].
  • Hong Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.

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