How to use <scp>AI</scp> in pathology

Schüffler, Peter J.; Steiger, Katja; Weichert, Wilko

doi:10.1002/gcc.23178

Cited by 6 publications

(1 citation statement)

References 55 publications

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“…Digital image analysis is emerging in several aspects of diagnostic pathology and opens up for performing standardized and very accurate quantifications. 29 , 37 , 38 , 39 However, knowledge of expected differences in the obtained results when applying this method is crucial. A pronounced difference was seen when comparing the manually obtained IEL counts with digital measurements in the surface epithelium.…”

Section: Discussionmentioning

confidence: 99%

Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method

Fiehn,

Engel,

Engel

et al. 2024

Journal of Pathology Informatics

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Section: Discussionmentioning

confidence: 99%

Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method

Fiehn,

Engel,

Engel

et al. 2024

Journal of Pathology Informatics

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A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes

Kazemi,

Rasouli-Saravani,

Gharib

et al. 2024

Computers in Biology and Medicine

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Deep learning detected histological differences between invasive and non‐invasive areas of early esophageal cancer

Urabe,

Adachi,

Sakamoto

et al. 2024

Cancer Science

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The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes occurring in invasive areas are still not well understood. This study aimed to explore the morphological differences between invasive and non‐invasive areas in early esophageal cancer specimens that have undergone endoscopic submucosal dissection (ESD), using artificial intelligence (AI) to shed light on the underlying mechanisms. In this study, data from 75 patients with esophageal squamous cell carcinoma (ESCC) were analyzed and endoscopic assessments were conducted to determine submucosal (SM) invasion. An AI model, specifically a Clustering‐constrained Attention Multiple Instance Learning model (CLAM), was developed to predict the depth of cancer by training on surface histological images taken from both invasive and non‐invasive regions. The AI model highlighted specific image portions, or patches, which were further examined to identify morphological differences between the two types of areas. The 256‐pixel AI model demonstrated an average area under the receiver operating characteristic curve (AUC) value of 0.869 and an accuracy (ACC) of 0.788. The analysis of the AI‐identified patches revealed that regions with invasion (SM) exhibited greater vascularity compared with non‐invasive regions (epithelial). The invasive patches were characterized by a significant increase in the number and size of blood vessels, as well as a higher count of red blood cells (all with p‐values <0.001). In conclusion, this study demonstrated that AI could identify critical differences in surface histopathology between non‐invasive and invasive regions, particularly highlighting a higher number and larger size of blood vessels in invasive areas.

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How to use AI in pathology

Cited by 6 publications

References 55 publications

Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method

Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method

A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes

Deep learning detected histological differences between invasive and non‐invasive areas of early esophageal cancer

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