Shi Ruoyu, Pinto João Correia, Rienda Ivan, Caie Peter, Eloy Catarina, Polónia António
Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore.
Department of Pathology and Laboratory Medicine, Kandang Kerbau Women´S and Children´S Hospital, Singapore, Singapore.
Virchows Arch. 2025 Mar;486(3):541-549. doi: 10.1007/s00428-024-03889-3. Epub 2024 Aug 7.
The aim of the present study was to develop and validate a quantitative image analysis (IA) algorithm to aid pathologists in assessing bright-field HER2 in situ hybridization (ISH) tests in solid cancers. A cohort of 80 sequential cases (40 HER2-negative and 40 HER2-positive) were evaluated for HER2 gene amplification with bright-field ISH. We developed an IA algorithm using the ISH Module from HALO software to automatically quantify HER2 and CEP17 copy numbers per cell as well as the HER2/CEP17 ratio. We observed a high correlation of HER2/CEP17 ratio, an average of HER2 and CEP17 copy number per cell between visual and IA quantification (Pearson's correlation coefficient of 0.842, 0.916, and 0.765, respectively). IA was able to count from 124 cells to 47,044 cells (median of 5565 cells). The margin of error for the visual quantification of the HER2/CEP17 ratio and of the average of HER2 copy number per cell decreased from a median of 0.23 to 0.02 and from a median of 0.49 to 0.04, respectively, in IA. Curve estimation regression models showed that a minimum of 469 or 953 invasive cancer cells per case is needed to reach an average margin of error below 0.1 for the HER2/CEP17 ratio or for the average of HER2 copy number per cell, respectively. Lastly, on average, a case took 212.1 s to execute the IA, which means that it evaluates about 130 cells/s and requires 6.7 s/mm. The concordance of the IA software with the visual scoring was 95%, with a sensitivity of 90% and a specificity of 100%. All four discordant cases were able to achieve concordant results after the region of interest adjustment. In conclusion, this validation study underscores the usefulness of IA in HER2 ISH testing, displaying excellent concordance with visual scoring and significantly reducing margins of error.
本研究的目的是开发并验证一种定量图像分析（IA）算法，以协助病理学家评估实体癌中的明场HER2原位杂交（ISH）检测。对80例连续病例（40例HER2阴性和40例HER2阳性）进行明场ISH检测，评估HER2基因扩增情况。我们使用HALO软件的ISH模块开发了一种IA算法，以自动定量每个细胞的HER2和CEP17拷贝数以及HER2/CEP17比值。我们观察到视觉定量与IA定量之间，HER2/CEP17比值、每个细胞HER2和CEP17拷贝数平均值具有高度相关性（Pearson相关系数分别为0.842、0.916和0.765）。IA能够计数124个细胞至47,044个细胞（中位数为5565个细胞）。在IA中，HER2/CEP17比值视觉定量的误差幅度以及每个细胞HER2拷贝数平均值的误差幅度分别从中位数0.23降至0.02和从中位数0.49降至0.04。曲线估计回归模型显示，为使HER2/CEP17比值或每个细胞HER2拷贝数平均值的平均误差幅度低于0.1，每例至少需要469个或953个浸润性癌细胞。最后，平均而言，一个病例执行IA需要212.1秒，这意味着它每秒可评估约130个细胞，每毫米需要6.7秒。IA软件与视觉评分的一致性为95%，敏感性为90%，特异性为100%。在调整感兴趣区域后，所有四个不一致的病例都能够获得一致的结果。总之，这项验证研究强调了IA在HER2 ISH检测中的有用性，与视觉评分显示出极好的一致性，并显著降低了误差幅度。
