Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies

Lipková, Jana; Chen, Tiffany; Lu, Ming; Chen, Richard J.; Shady, Maha; Williams, Mane; Wang, Jingwen; Noor, Zahra; Mitchell, Richard N.; Turan, Mehmet; Coskun, Gulfize; Yılmaz, Funda; Demır, Derya; Nart, Denız; Başak, Kayhan; Turhan, Nesrin; Özkara, Selvinaz; Banz, Yara; Odening, Katja E.; Mahmood, Faisal

doi:10.1038/s41591-022-01709-2

Cited by 57 publications

(42 citation statements)

References 45 publications

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“…Given the limited availability and heterogeneous nature of these plaques, an ideal masking method will retain all the plaque components, which can be used to discover tissue or compartment-specific markers for disease progression and phenotyping. This is also a valuable feature of EntropyMasker, which can be implemented at the preprocessing stage of various deep learning pipelines for other diseases and research areas which require a complete WSI masking step [45][46][47] .…”

Section: Conclusion and Discussionmentioning

confidence: 99%

An automatic entropy method to efficiently mask histology whole-slide images

Song

Cisternino

Mekke

et al. 2022

Preprint

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Background: Tissue segmentation of histology whole-slide images (WSI) remains a critical task in automated digital pathology workflows for both accurate disease diagnosis and deep phenotyping for research purposes. This is especially challenging when the tissue structure of biospecimens is relatively porous and heterogeneous, such as for atherosclerotic plaques. Methods: In this study, we developed a unique approach called EntropyMasker based on image entropy to tackle the fore- and background segmentation (masking) task in histology WSI. We evaluated our method on 97 high-resolution WSI of human carotid atherosclerotic plaques in the Athero-Express Biobank Study, constituting hematoxylin and eosin (H&E) and 8 other staining types. Results and Conclusion: Using multiple benchmarking metrics, we compared our method with four widely used segmentation methods: Otsu's method, Adaptive mean, Adaptive Gaussian and slideMask and observed that our method had the highest sensitivity and Jaccard similarity index. We envision EntropyMasker to fill an important gap in WSI preprocessing and deep learning image analysis pipelines and enable disease phenotyping beyond the field of atherosclerosis.

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Section: Conclusion and Discussionmentioning

confidence: 99%

An automatic entropy method to efficiently mask histology whole-slide images

Song

Cisternino

Mekke

et al. 2022

Preprint

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“…Hong et al (54) have also acknowledged this architectural limitation, reporting that non-tumor tiles were often given inconclusive prediction scores. The discrepancy between tile and slide-level classification labels remains a well-known challenge in the field, which has started transitioning to more state-of-the-art DL architectures promising better performance (97)(98)(99)(100).…”

Section: Deep Learning Can Recognize Phenotypes Of Mutations On Hande...mentioning

confidence: 99%

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

et al. 2022

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Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.

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“…For deployment, we used all of the slides' tiles. We compared our approach to CRANE as presented by Lipkova et al [16]. To do so, we followed the workflow of the CRANE study, preprocessing the slides with the CLAM repository and performed 10-fold Monte-Carlo cross validation on our training cohort, deploying the best performing model on our test cohorts [23].…”

Section: Deep Learning Workflowmentioning

confidence: 99%

“…Precedent cases exist in the prediction of organ rejection after kidney transplantation [13], as well as applications of simple, handcrafted feature based image analysis methods to cardiac biopsies after transplantation [14,15]. A recent study by Lipkova et al used the Deep Learning pipeline “CRANE” to predict cardiac allograft transplantation, yielding a very high and clinical-grade performance [16,17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of heart transplant rejection from routine pathology slides with self-supervised Deep Learning

Seraphin

Luedde

Roderburg

et al. 2022

Preprint

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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. We collected 1079 slides from 325 patients from three transplant centers 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, 0.913, and 0.631, 0.633, 0.682, and 0.722, 0.601, 0.805 in the validation cohorts, respectively. The predictions of the AI model were interpretable by human experts and highlighted plausible morphological patterns. We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small patient cohorts.

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Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies

Cited by 57 publications

References 45 publications

An automatic entropy method to efficiently mask histology whole-slide images

An automatic entropy method to efficiently mask histology whole-slide images

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Prediction of heart transplant rejection from routine pathology slides with self-supervised Deep Learning

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