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Review
. 2023 Jan 31:14:100298.
doi: 10.1016/j.jpi.2023.100298. eCollection 2023.

Imaging bridges pathology and radiology

Hansmann Martin-Leo  1   2 , Klauschen Frederick  3   4   5   6   7 , Samek Wojciech  5   8   9 , Müller Klaus-Robert  5   7   9   10   11 , Donnadieu Emmanuel  12 , Scharf Sonja  1   2   13 , Hartmann Sylvia  14 , Koch Ina  13 , Ackermann Jörg  13 , Pantanowitz Liron  15 , Schäfer Hendrik  2   3 , Wurzel Patrick  1   2   13
Affiliations
Review

Imaging bridges pathology and radiology

Hansmann Martin-Leo et al. J Pathol Inform. .

Abstract

In recent years, medical disciplines have moved closer together and rigid borders have been increasingly dissolved. The synergetic advantage of combining multiple disciplines is particularly important for radiology, nuclear medicine, and pathology to perform integrative diagnostics. In this review, we discuss how medical subdisciplines can be reintegrated in the future using state-of-the-art methods of digitization, data science, and machine learning. Integration of methods is made possible by the digitalization of radiological and nuclear medical images, as well as pathological images. 3D histology can become a valuable tool, not only for integration into radiological images but also for the visualization of cellular interactions, the so-called connectomes. In human pathology, it has recently become possible to image and calculate the movements and contacts of immunostained cells in fresh tissue explants. Recording the movement of a living cell is proving to be informative and makes it possible to study dynamic connectomes in the diagnosis of lymphoid tissue. By applying computational methods including data science and machine learning, new perspectives for analyzing and understanding diseases become possible.

Keywords: 3D/4D histology; Computer-assisted detection; Digital pathology; Imaging; Machine learning; Nuclear medicine; Radiology.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Workflow to visualize and analyze organs and tissues in radiology and pathology. The flow chart illustrates possibilities to combine disciplines.
Fig. 2
Fig. 2
Sinus network of a human lymph node visualized by confocal microscopy. The tissue sample was immunostained with smooth-muscle actin (green) to visualize fibroblastic reticular cells and CD68 (red) to highlight macrophages. The impact of different tissue preparation and image processing methods is shown.
Fig. 3
Fig. 3
Movement study of lymphadenitis with confocal laser. Left picture: A confocal image showing the network of follicular dendritic cells (FDC) stained with an anti-CD35 antibody (green), and follicular T helper cells visualized with an anti-PD1 (red) antibody. Middle picture: An image showing the automated detected cells using the spot function of the Imaris software. Right picture: The colors of vectors indicate low speed (blue), medium speed (green), and high speed (red) of follicular T helper cells during a 20-min recording.
Fig. 4
Fig. 4
Features of holistic diagnostics for the incorporation of methods from pathology, radiology, and nuclear medicine.
See this image and copyright information in PMC

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