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High-accuracy prostate cancer pathology using deep learning

Nature Machine Intelligence volume 2, pages 411–418 (2020)Cite this article

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Abstract

Deep learning (DL) is a powerful methodology for the recognition and classification of tissue structures in digital pathology. Its performance in prostate cancer pathology is still under intensive investigation. Here we develop DL-based models for the detection of prostate cancer tissue in whole-slide images based on a large high-quality annotated training dataset and a modern state-of-the-art convolutional network architecture (NASNetLarge). The overall accuracy of our model for tumour detection in two validation cohorts is comparable to that of pathologists and reaches 97.3% in a native version and more than 98% using the suggested DL-based augmentation strategies. As a second step, we suggest a new biologically meaningful DL-based algorithm for Gleason grading of prostatic adenocarcinomas with high, human-level performance in prognostic stratification of patients when tested in several well-characterized validation cohorts. Furthermore, we determine the optimal minimal tumour size (real size of approximately 560 × 560 µm) for robust Gleason grading representative of the whole tumour focus. Our approach is realized in the unified digital pathology pipeline, which delivers all the relevant tumour metrics for a pathology report.

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Fig. 1: Composition of training and validation cohorts.
Fig. 2: Analysis of the model accuracy.
Fig. 3: Pipeline for analysis of prostatectomy WSIs.
Fig. 4: Tests of grading accuracy for three pathologists and DL-based model.
Fig. 5: A grading part of pipeline for processing of WSIs.

Data availability

The whole-slide images used for algorithm development (training dataset) are publicly available through GDC Data Portal of the National Cancer Institute (The Cancer Genome Atlas Project; http://portal.gdc.cancer.gov). The validation datasets (image patches of tumour and benign classes) generated and analysed during the current study are available for academical use only from public repository https://zenodo.org/deposit/3825933. Any usage for publications should be consented by corresponding authors. All other data may be obtained upon request to the authors.

Code availability

The source code used in this study is available at https://github.com/gagarin37/deep_learning_pca.

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Acknowledgements

The GPU card for this study was donated by the NVIDIA corporation (a GPU academic grant programme). No funding was received for this study.

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Authors and Affiliations

  1. Institute of Pathology, University Hospital Cologne, Cologne, Germany

    Yuri Tolkach

  2. Institute of Pathology, University Hospital Bonn, Bonn, Germany

    Yuri Tolkach, Tilmann Dohmgörgen, Marieta Toma & Glen Kristiansen

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  1. Yuri Tolkach
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  2. Tilmann Dohmgörgen
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  3. Marieta Toma
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  4. Glen Kristiansen
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Contributions

Conceiving the study and the design: Y.T. Annotations, creating the datasets, developing and training the models, development of the grading algorithm and augmentation strategies, digital pathology pipeline, validation tests, hardware, conducting the statistical analysis and data analysis: Y.T. Providing validation datasets: G.K. Grading for validation experiments: Y.T., G.K. and M.T. Data interpretation: Y.T. and G.K. Drafting the manuscript: Y.T. Critically revising for important intellectual content: Y.T., T.D., M.T. and G.K.

Corresponding authors

Correspondence to Yuri Tolkach or Glen Kristiansen.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Summary of study design and setup.

Study design and setup of single study steps, principles of patch generation (including number of patches generated in training and validation datasets in thousands of unique non-intersecting patches, k), approaches to training and validation. For details to certain steps see Methods.

Extended Data Fig. 2 Training principles of the model.

Three classes were used for training (benign glandular, benign non-glandular, tumour) with stain normalization. NASNetLarge architecture was used with addition of three new layers for classification (flatten, fully connected, and classification layers). Probability of being benign could be summarized from output probabilities of two benign classes. Training principles and parameters are presented in boxes.

Extended Data Fig. 3 Deep learning – based prostate cancer grading.

a. Prostate cancer (PCA) architecture is not just a rough mix of glands with Gleason patterns (GP) 3, 4, and 5. In three dimensions, carcinoma is a tree, where single GPs do not exist, being a continuum. b. Our Gleason grading algorithm is based on the understanding of GPs being a continuum. During processing, a tumour region is being cut into patches that are then separately analyzed by convnet (trained on pure GPs, see Materials and Methods). Probabilities of different classes (GP3, GP4, GP5) are counted for every single patch and summarized for the whole tumour region and not used for nomination of every patch to a discrete GP (as in classical paradigm).

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Tolkach, Y., Dohmgörgen, T., Toma, M. et al. High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell 2, 411–418 (2020). https://doi.org/10.1038/s42256-020-0200-7

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