Differences between computer-assisted image analysis (CAI) algorithms may cause discrepancies in the identification of immunohistochemically stained immune biomarkers in biopsies of breast cancer patients. These discrepancies have implications for their association with disease outcome. This study aims to compare three CAI procedures (A, B and C) to measure positive marker areas in post-neoadjuvant chemotherapy biopsies of patients with triple-negative breast cancer (TNBC) and to explore the differences in their performance in determining the potential association with relapse in these patients. A total of 3304 digital images of biopsy tissue obtained from 118 TNBC patients were stained for seven immune markers using immunohistochemistry (CD4, CD8, FOXP3, CD21, CD1a, CD83, HLA-DR) and were analyzed with procedures A, B and C. The three methods measure the positive pixel markers in the total tissue areas. The extent of agreement between paired CAI procedures, a principal component analysis (PCA) and Cox multivariate analysis was assessed. Comparisons of paired procedures showed close agreement for most of the immune markers at low concentration. The probability of differences between the paired procedures B/C and B/A was generally higher than those observed in C/A. The principal component analysis, largely based on data from CD8, CD1a and HLA-DR, identified two groups of patients with a significantly lower probability of relapse than the others. The multivariate regression models showed similarities in the factors associated with relapse for procedures A and C, as opposed to those obtained with procedure B. General agreement among the results of CAI procedures would not guarantee that the same predictive breast cancer markers were consistently identified. These results highlight the importance of developing additional strategies to improve the sensitivity of CAI procedures.

计算机辅助图像分析 (CAI) 算法之间的差异可能会导致乳腺癌患者活检中免疫组织化学染色免疫生物标志物的鉴定存在差异。这些差异对它们与疾病结果的关联有影响。本研究旨在比较三种 CAI 程序（A、B 和 C）以测量三阴性乳腺癌 (TNBC) 患者新辅助化疗后活检中的阳性标志物区域，并探讨它们在确定潜在关联方面的表现差异在这些患者中复发。使用免疫组织化学（CD4、CD8、FOXP3、CD21、CD1a、CD83、HLA-DR）对来自 118 名 TNBC 患者的活检组织的总共 3304 幅数字图像进行染色，并使用程序 A、B 和 C 进行分析。这三种方法测量总组织区域中的阳性像素标记。评估了配对 CAI 程序、主成分分析 (PCA) 和 Cox 多变量分析之间的一致性程度。配对程序的比较显示，大多数免疫标记在低浓度时非常一致。配对程序 B/C 和 B/A 之间的差异概率通常高于在 C/A 中观察到的概率。主要基于来自 CD8、CD1a 和 HLA-DR 的数据的主成分分析确定了两组患者的复发概率明显低于其他患者。多元回归模型显示，程序 A 和 C 的复发相关因素与使用程序 B 获得的因素相似。CAI 程序结果之间的普遍一致性并不能保证一致地确定相同的预测性乳腺癌标志物。这些结果强调了制定额外策略以提高 CAI 程序敏感性的重要性。