Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours

Iizuka, Osamu; Kanavati, Fahdi; Kato, Kei; Rambeau, Michael; Arihiro, Koji; Tsuneki, Masayuki

doi:10.1038/s41598-020-58467-9

Cited by 343 publications

(326 citation statements)

References 30 publications

(31 reference statements)

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“…There are several deep learning models that have been developed for the diagnostic of gastric cancers using whole slide images (11)(12)(13)15,24) and a comparison of some of these studies and ours is summarized in Supplementary Table S9. Although each study adopted a results of 0.97-0.99 AUROC in the prospective set.…”

Section: Discussionmentioning

confidence: 99%

“…Iizuka et al evaluated their algorithm that classifies WSIs into either non-neoplastic, adenoma, or adenocarcinoma, and confirmed accuracy of 95.6% from their test set consisting of 45 WSIs. (13) Although the aforementioned CNN-based gastric cancer detection algorithms have shown promising results, their diagnostic performance in the real clinical setting as well as impact on diagnostic workflow has not been validated.…”

Section: Introductionmentioning

confidence: 99%

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A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Park¹,

Jang

Kim³

et al. 2021

Clinical Cancer Research

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Purpose: Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are continuously increasing, and there is a need for an automatic computerized diagnostic system to reduce the diagnostic burden. In this study, we developed an algorithm to classify gastric epithelial tumors automatically and assessed its performance in a large series of gastric biopsies and its benefits as an assistance tool. Experimental Design: Using 2,434 whole-slide images, we developed an algorithm based on convolutional neural networks to classify a gastric biopsy image into one of three categories: negative for dysplasia (NFD), tubular adenoma, or carcinoma. The performance of the algorithm was evaluated by using 7,440 biopsy specimens collected prospectively. The impact of algorithm-assisted diagnosis was assessed by six pathologists using 150 gastric biopsy cases. Results: Diagnostic performance evaluated by the AUROC curve in the prospective study was 0.9790 for two-tier classification: negative (NFD) versus positive (all cases except NFD). When limited to epithelial tumors, the sensitivity and specificity were 1.000 and 0.9749. Algorithm-assisted digital image viewer (DV) resulted in 47% reduction in review time per image compared with DV only and 58% decrease to microscopy. Conclusions: Our algorithm has demonstrated high accuracy in classifying epithelial tumors and its benefits as an assistance tool, which can serve as a potential screening aid system in diagnosing gastric biopsy specimens.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Park¹,

Jang

Kim³

et al. 2021

Clinical Cancer Research

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“…Currently, CNNs are applied not only in digital pathology, but also in computed tomography (CT) scans, ophthalmoscope images, and ultrasound images ( Esteva et al, 2017 ; De Fauw et al, 2018 ; Coudray et al, 2018 ; Philbrick et al, 2018 ; Falk et al, 2019 ; Li et al, 2019 ). Studies ( Macenko et al, 2009 ; Iizuka et al, 2020 ) have revealed that AI can identify various lesions with a level of competence observed by imaging experts.…”

Section: Discussionmentioning

confidence: 99%

“…A simple CNN architecture for automatic classification of GC using WSIs in histopathology has been described by Sharma et al ( Sharma et al, 2017b ), thereby revealing the practicability of AI in digital-pathology research for GC. However, their work has rarely focused on how the deep-learning framework identifies GC lesions, nor how the results might influence the prognosis ( Droste et al, 2019 ; Iizuka et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Guo

Hu³

et al. 2020

Front. Pharmacol.

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Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.

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“…(14)(15)(16) This expands upon our preliminary model, as with the use of ResNet50 it provides powerful analytics for images. Further, the input to the previously published model (30) and other similar published studies for other clinical diseases consisted of small patches of whole slide images (15,34) that represent approximately 2% of the original biopsy image. Such patch inputs do not take into account the fact that tissue features can be better visualized at multiple magnifications.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection

Syed

Ehsan

Shrivastava

et al. 2020

Preprint

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Objectives: Striking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs: ResNet and custom Shallow CNN). Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the decision making process of the models. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAM visualizations to confirm structural preservation and biological relevance, respectively. Results: 461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0 to 121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated ability of the models to learn distinct microscopic morphological features. Conclusion: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features, emulating human pathologist decision making process, performing in the case of suboptimal computational environment, and being modified for improving disease classification accuracy. Grad-CAMs that were employed illuminated the otherwise black box of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

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Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours

Cited by 343 publications

References 30 publications

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Artificial Intelligence-Based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection

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