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<p>Histopathological diagnosis relies on careful and expert assessment of tissue as guided by multiple criteria relevant to specific immunohistochemical (IHC) markers. Computer-aided detection or diagnosis systems have recently been deployed to detect abnormalities in histological samples, transforming many areas of research and medicine such as pathology. These software packages can provide a helpful decision support tool for accelerating analysis, but they would need to capture information from the sample in a manner that facilitates the multicriteria assessment/interpretation demanded by the IHC markers and other histochemical stains. As a result of this potential, and the limited assessment of the performance of software utilized for automated analysis of histological samples, we conducted this study. We aimed to provide a technical assessment of two analysis approaches that are utilized in two commercially available image analysis software platforms, namely positive pixel count analysis approach and cell-by-cell analysis approach. These two approaches are used in many digital histopathological slide analysis software packages including ImageScope (Leica Biosystems) and HALO (Indica Labs), which respectively deploy the aforementioned algorithms and thus were used as proxies for the comparison in this study. Thirty-seven whole slide images of immunohistochemically stained tumor samples from breast, colon, and endometrium were analyzed using three different sampling methods recording percentage of antibody marker positivity. The pixel-based software was better able to identify color intensity, offering the option for grading the IHC marker. However, the object-based software outperformed pixel-based software, having more consistent positivity estimates across the three sampling methods. These results are limited by the small number of clinical samples, IHC marker heterogeneity, and the lack of ground-truth data. Nonetheless, neither of the software packages’ metrics performed in a manner required for comprehensive assessment of the IHC markers in this study, yet they can be used to address specific questions related to quantitative expression of tumor diagnostic markers.</p>

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A comparison of pixel intensity-based and object-based image analysis software algorithms for assessing immunohistochemical staining of sections from paraffin-embedded human tumor samples

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Sarah E. Kimambo,
  • Josh Overton,
  • Nicole A. Bouffard,
  • Kyra Lee,
  • Abiy Ambaye,
  • Douglas J. Taatjes

摘要

Histopathological diagnosis relies on careful and expert assessment of tissue as guided by multiple criteria relevant to specific immunohistochemical (IHC) markers. Computer-aided detection or diagnosis systems have recently been deployed to detect abnormalities in histological samples, transforming many areas of research and medicine such as pathology. These software packages can provide a helpful decision support tool for accelerating analysis, but they would need to capture information from the sample in a manner that facilitates the multicriteria assessment/interpretation demanded by the IHC markers and other histochemical stains. As a result of this potential, and the limited assessment of the performance of software utilized for automated analysis of histological samples, we conducted this study. We aimed to provide a technical assessment of two analysis approaches that are utilized in two commercially available image analysis software platforms, namely positive pixel count analysis approach and cell-by-cell analysis approach. These two approaches are used in many digital histopathological slide analysis software packages including ImageScope (Leica Biosystems) and HALO (Indica Labs), which respectively deploy the aforementioned algorithms and thus were used as proxies for the comparison in this study. Thirty-seven whole slide images of immunohistochemically stained tumor samples from breast, colon, and endometrium were analyzed using three different sampling methods recording percentage of antibody marker positivity. The pixel-based software was better able to identify color intensity, offering the option for grading the IHC marker. However, the object-based software outperformed pixel-based software, having more consistent positivity estimates across the three sampling methods. These results are limited by the small number of clinical samples, IHC marker heterogeneity, and the lack of ground-truth data. Nonetheless, neither of the software packages’ metrics performed in a manner required for comprehensive assessment of the IHC markers in this study, yet they can be used to address specific questions related to quantitative expression of tumor diagnostic markers.