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Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning

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Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning. / Seraphin, Tobias Paul; Luedde, Mark; Roderburg, Christoph; van Treeck, Marko; Scheider, Pascal; Buelow, Roman D; Boor, Peter; Loosen, Sven H; Provaznik, Zdenek; Mendelsohn, Daniel; Berisha, Filip; Magnussen, Christina; Westermann, Dirk; Luedde, Tom; Brochhausen, Christoph; Sossalla, Samuel; Kather, Jakob Nikolas.

In: European heart journal. Digital health, Vol. 4, No. 3, 05.2023, p. 265-274.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Seraphin, TP, Luedde, M, Roderburg, C, van Treeck, M, Scheider, P, Buelow, RD, Boor, P, Loosen, SH, Provaznik, Z, Mendelsohn, D, Berisha, F, Magnussen, C, Westermann, D, Luedde, T, Brochhausen, C, Sossalla, S & Kather, JN 2023, 'Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning', European heart journal. Digital health, vol. 4, no. 3, pp. 265-274. https://doi.org/10.1093/ehjdh/ztad016

APA

Seraphin, T. P., Luedde, M., Roderburg, C., van Treeck, M., Scheider, P., Buelow, R. D., Boor, P., Loosen, S. H., Provaznik, Z., Mendelsohn, D., Berisha, F., Magnussen, C., Westermann, D., Luedde, T., Brochhausen, C., Sossalla, S., & Kather, J. N. (2023). Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning. European heart journal. Digital health, 4(3), 265-274. https://doi.org/10.1093/ehjdh/ztad016

Vancouver

Seraphin TP, Luedde M, Roderburg C, van Treeck M, Scheider P, Buelow RD et al. Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning. European heart journal. Digital health. 2023 May;4(3):265-274. https://doi.org/10.1093/ehjdh/ztad016

Bibtex

@article{8e2b31d64f784f8f83207dee89ffbb5f,
title = "Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning",
abstract = "AIMS: One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.METHODS AND RESULTS: We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns.CONCLUSION: We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.",
author = "Seraphin, {Tobias Paul} and Mark Luedde and Christoph Roderburg and {van Treeck}, Marko and Pascal Scheider and Buelow, {Roman D} and Peter Boor and Loosen, {Sven H} and Zdenek Provaznik and Daniel Mendelsohn and Filip Berisha and Christina Magnussen and Dirk Westermann and Tom Luedde and Christoph Brochhausen and Samuel Sossalla and Kather, {Jakob Nikolas}",
note = "{\textcopyright} The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.",
year = "2023",
month = may,
doi = "10.1093/ehjdh/ztad016",
language = "English",
volume = "4",
pages = "265--274",
journal = "European heart journal. Digital health",
issn = "2634-3916",
number = "3",

}

RIS

TY - JOUR

T1 - Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning

AU - Seraphin, Tobias Paul

AU - Luedde, Mark

AU - Roderburg, Christoph

AU - van Treeck, Marko

AU - Scheider, Pascal

AU - Buelow, Roman D

AU - Boor, Peter

AU - Loosen, Sven H

AU - Provaznik, Zdenek

AU - Mendelsohn, Daniel

AU - Berisha, Filip

AU - Magnussen, Christina

AU - Westermann, Dirk

AU - Luedde, Tom

AU - Brochhausen, Christoph

AU - Sossalla, Samuel

AU - Kather, Jakob Nikolas

N1 - © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.

PY - 2023/5

Y1 - 2023/5

N2 - AIMS: One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.METHODS AND RESULTS: We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns.CONCLUSION: We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.

AB - AIMS: One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.METHODS AND RESULTS: We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns.CONCLUSION: We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.

U2 - 10.1093/ehjdh/ztad016

DO - 10.1093/ehjdh/ztad016

M3 - SCORING: Journal article

C2 - 37265858

VL - 4

SP - 265

EP - 274

JO - European heart journal. Digital health

JF - European heart journal. Digital health

SN - 2634-3916

IS - 3

ER -