Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Shmatko, Artem; Laleh, Narmin Ghaffari; Gerstung, Moritz; Kather, Jakob Nikolas

doi:10.1038/s43018-022-00436-4

Cited by 154 publications

(146 citation statements)

References 137 publications

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“…Intriguingly, we observed significant differences between Hover-Net- and HD-Staining-predicted cell types within stromal and necrotic regions annotated by our pathologist. These region-level annotations make possible a more systematic comparison of Hover-Net, HD-Staining, and related methods across other tumor types (Shmatko et al 2022). This remains for future work.…”

Section: Discussionmentioning

confidence: 99%

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

White

Woo

Koc

et al. 2022

Preprint

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Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

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Section: Discussionmentioning

confidence: 99%

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

White

Woo

Koc

et al. 2022

Preprint

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“…We used a weakly supervised end-to-end prediction workflow for binary classification tasks [1,3]. "Weakly supervised" in this context means that the target labels are only defined on the level of whole-slide images, but the actual computational analysis is performed on the level of tiles.…”

Section: End-to-end Prediction Workflowmentioning

confidence: 99%

“…Computational pathology refers to the use of deep learning (DL) methods in histopathology [1,2]. DL can predict molecular biomarkers directly from routine tissue slides, which could be a helpful tool in precision oncology of solid tumors [3,4]. Several molecular biomarkers are used to guide treatment in advanced and metastatic gastric cancer.…”

Section: Introductionmentioning

confidence: 99%

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning

et al. 2022

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Background Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL). Methods Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein–Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer. Results On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance. Conclusions Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability.

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“…Therefore, pathologists use it broadly to prevail over the subjective visual assessment obstacles and merge other computations for more exactitude in treating tumors [ 10 ]. DL models have numerous advantages in the histopathology field, including the ability to work with unstructured data and to generate new features with high quality from datasets without human intervention, which improves the accuracy of diagnosis and leads to the optimization of the treatment protocol [ 11 ]. The multiple layers in the neural network enhance the self-learning ability while operating intensive computational tasks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches in Histopathology

Ahmed

Abouzid

Kaczmarek

2022

Cancers

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The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers’ routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.

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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Cited by 154 publications

References 137 publications

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning

Deep Learning Approaches in Histopathology

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