Ki‐67 evaluation using deep‐learning model‐assisted digital image analysis in breast cancer

Aims To test the efficacy of artificial intelligence (AI)‐assisted Ki‐67 digital image analysis in invasive breast carcinoma (IBC) with quantitative assessment of AI model performance. Methods and Results This study used 494 cases of Ki‐67 slide images of IBC core needle biopsies. The methods were divided into two steps: (i) construction of a deep‐learning model (DL); and (ii) DL implementation for Ki‐67 analysis. First, a DL tissue classifier model (DL‐TC) and a DL nuclear detection model (DL‐ND) were constructed using HALO AI DenseNet V2 algorithm with 31,924 annotations in 300 Ki‐67 digital slide images. Whether the class predicted by DL‐TC in the test set was correct compared with the annotation of ground truth at the pixel level was evaluated. Second, DL‐TC‐ and DL‐ND‐assisted digital image analysis (DL‐DIA) was performed in the other 194 luminal‐type cases and correlations with manual counting and clinical outcome were investigated to confirm the accuracy and prognostic potential of DL‐DIA. The performance of DL‐TC was excellent and invasive carcinoma nests were well segmented from other elements (average precision: 0.851; recall: 0.878; F1‐score: 0.858). Ki‐67 index data and the number of nuclei from DL‐DIA were positively correlated with data from manual counting ( ρ = 0.961, and 0.928, respectively). High Ki‐67 index (cutoff 20%) cases showed significantly worse recurrence‐free survival and breast cancer‐specific survival ( P = 0.024, and 0.032, respectively). Conclusion The performances of DL‐TC and DL‐ND were excellent. DL‐DIA demonstrated a high degree of concordance with manual counting of Ki‐67 and the results of this approach have prognostic potential.