Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study.

Autor: Teng Zhu, Yu-Hong Huang, Wei Li, Yi-Min Zhang, Ying-Yi Lin, Min-Yi Cheng, Zhi-Yong Wu, Guo-Lin Ye, Ying Lin, Kun Wang
Zdroj: International Journal of Surgery; Nov2023, Vol. 109 Issue 11, p3383-3394, 12p
Abstrakt: Background: The high false negative rate (FNR) associated with sentinel lymph node biopsy often leads to unnecessary axillary lymph node dissection following neoadjuvant chemotherapy (NAC) in breast cancer. The authors aimed to develop a multifactor artificial intelligence (AI) model to aid in axillary lymph node surgery. Materials and Methods: A total of 1038 patients were enrolled, comprising 234 patients in the primary cohort, 723 patients in three external validation cohorts, and 81 patients in the prospective cohort. For predicting axillary lymph node response to NAC, robust longitudinal radiomics features were extracted from pre-NAC and post-NAC magnetic resonance images. The U test, the least absolute shrinkage and selection operator, and the spearman analysis were used to select the most significant features. A machine learning stacking model was constructed to detect ALNmetastasis after NAC. By integrating the significant predictors, we developed amultifactor AI-assisted surgery pipeline and compared its performance and false negative rate with that of sentinel lymph node biopsy alone. Results: The machine learning stacking model achieved excellent performance in detecting ALN metastasis, with an area under the curve (AUC) of 0.958 in the primary cohort, 0.881 in the external validation cohorts, and 0.882 in the prospective cohort. Furthermore, the introduction of AI-assisted surgery reduced the FNRs from 14.88 (18/121) to 4.13%(5/121) in the primary cohort, from 16.55 (49/296) to 4.05% (12/296) in the external validation cohorts, and from 13.64 (3/22) to 4.55% (1/22) in the prospective cohort. Notably, when more than two SLNs were removed, the FNRs further decreased to 2.78% (2/72) in the primary cohort, 2.38% (4/168) in the external validation cohorts, and 0% (0/15) in the prospective cohort. Conclusion: Our study highlights the potential of AI-assisted surgery as a valuable tool for evaluating ALN response to NAC, leading to a reduction in unnecessary axillary lymph node dissection procedures. [ABSTRACT FROM AUTHOR]
Databáze: Supplemental Index