AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?

Cifci, Didem; Veldhuizen, Gregory Patrick; Foersch, Sebastian; Kather, Jakob Nikolas

doi:10.1146/annurev-cancerbio-061521-092038

Cited by 16 publications

(6 citation statements)

References 81 publications

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“…Computational approaches to understanding tissue and cellular information from histology slides have greatly improved in recent years. Specifically, tools to segment or classify of malignant tissues and cells from biopsy specimens [51], and cell type classification on All rights reserved. No reuse allowed without permission.…”

Section: Discussionmentioning

confidence: 99%

Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Bell¹,

Brendel²,

Konnaris³

et al. 2023

Preprint

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Rheumatoid arthritis (RA) is a complex immune-mediated inflammatory disorder in which patients suffer from inflammatory-erosive arthritis. Recent advances on histopathology heterogeneity of RA pannus tissue revealed three distinct phenotypes based on cellular composition (pauci-immune, diffuse and lymphoid), suggesting distinct etiologies that warrant specific targeted therapy. Thus, cost-effective alternatives to clinical pathology phenotyping are needed for research and disparate healthcare. To this end, we developed automated multi-scale computational pathotyping (AMSCP) with two distinct components that can be leveraged together or independently: 1) segmentation of different tissue types to characterize tissue-level changes, and 2) cell type classification within each tissue compartment that assesses change across disease states. Initial training and validation were completed on 264 knee histology sections from mice with TNF-transgenic (n=233) and injected zymosan induced (n=32) inflammatory arthritis. Peak tissue segmentation performance was 0.94 +/- 0.01 frequency weight mean intersection over union and peak cell classification was 0.83 +/- 0.12 F1. We then leveraged these models and adapted them to analyze RA pannus tissue clinically phenotyped as pauci-immune (n=5), diffuse (n=28) and lymphoid (n=27), achieving peak cell classification of 0.81 +/- 0.06 F1. Regression analysis demonstrated a highly significant correlation between AMSCP of lymphocyte counts and average Krenn Inflammation Score (rho = 0.88; p<0.0001). While a simple threshold of 1.1% of plasma cells demonstrated the phenotyping potential of our automated approach vs. a clinical pathologist with a sensitivity and specificity of 0.81 and 0.73. Taken together, we find AMSCP to be a valuable cost-effective method for research. Follow up studies to assess its clinical utility are warranted.

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Section: Discussionmentioning

confidence: 99%

Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Bell¹,

Brendel²,

Konnaris³

et al. 2023

Preprint

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“…AI is being used successfully in various histological image analysis steps (113). The classical application is to replicate tasks performed by humans, with a side effect of higher reproducibility, such as in cell detection (114) and tissue segmentation (2).…”

Section: Strongly Supervised Learning In Histopathologymentioning

confidence: 99%

Toward Explainable Artificial Intelligence for Precision Pathology

Klauschen,

Dippel,

Keyl

et al. 2024

Annu. Rev. Pathol. Mech. Dis.

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The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 19 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…However, the application of AI in medicine, particularly in cancer research, faces unique challenges due to the complexity and multi-dimensionality of the field. 172,173 Technical challenges in developing and validating generalizable solutions across diverse populations are significant. Furthermore, the potential for inner biases and misdirection of algorithms poses a growing concern, especially when these AI tools reflect the unconscious biases of their creators.…”

Section: Artificial Intelligencementioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

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AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?

Cited by 16 publications

References 81 publications

Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Toward Explainable Artificial Intelligence for Precision Pathology

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

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