Abstract: Objective To explore the application value of artificial intelligence assisted imaging in evaluating the effect of neoadjuvant chemotherapy for locally advanced breast cancer.Methods A total of 107 patients with locally advanced breast cancer were admitted from February 2023 to February 2025.107 patients with locally advanced breast cancer who were treated with EC-T (Epirubicin+Cyclophosphamide sequential Taxotere) sequential neoadjuvant chemotherapy were examined by magnetic resonance imaging (MRI) before and after chemotherapy.According to the Miller-Payne pathological evaluation system,patients were divided into an effective group and an ineffective group.The MRI parameters between the two groups after chemotherapy was compared,and the relationship between MRI parameters and ineffective neoadjuvant chemotherapy was investigated using multiple Logistic regression analysis.The MRI imaging evaluation model based on artificial intelligence was constructed,and this model was used to evaluate the effectiveness of neoadjuvant chemotherapy.Results 76 patients (71.03%) were effective in chemotherapy and 31 (28.97%) were ineffective among the 107 patients.The maximum tumor diameter,apparent diffusion coefficient (ADC),volume transfer constant (Ktrans),extracellular space volume percentage (Ve),rate constant (Kep),maximum enhancement ratio (Emax),maximum enhancement rate (Vmax),contrast enhancement rate (CER) and peak time (Tmax) in the effective group were (3.96±0.83)cm,(1.21±0.28)×10-3mm2/s,(0.41±0.07)min-1,(0.79±0.14)%,(0.52±0.15)min-1,(91.25±25.64)%,(0.55±0.12)SI/S,(1.48±0.32) and (301.36±40.78)s respectively，which in the ineffective group were (6.95±1.03)cm,(0.88±0.16)×10-3mm2/s,(0.88±0.19)min-1,(1.03±0.12)%,(0.85±0.23)min-1,(153.78±32.03)%,(0.91±0.24)SI/S,(1.64±0.35) and (169.92±34.71)s respectively,and the differences between the two groups were statistically significant (P<0.05).Multivariate logistic regression analysis showed that the maximum tumor diameter,Ktrans,Ve,Kep,Emax,Vmax and CER were risk factors for the ineffectiveness of neoadjuvant chemotherapy for locally advanced breast cancer (P<0.05),and ADC and CER were protective factors (P<0.05).The sensitivity,specificity,positive predictive value,negative predictive value and accuracy of the imaging evaluation model based on artificial intelligence to evaluate the effect of neoadjuvant chemotherapy for locally advanced breast cancer were 93.55%,94.74%,87.88%,97.30% and 94.39% respectively,and the evaluation result of this model was extremely strong consistency with pathological evaluation (Kappa=0.866,P<0.05).Conclusion Artificial intelligence assisted imaging has good application value in evaluating the effect of neoadjuvant chemotherapy for locally advanced breast cancer.

Key words: artificial intelligence, magnetic resonance imaging, breast cancer, neoadjuvant chemotherapy, clinical efficacy