Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis

Bédard, Agathe; Westerling-Bui, Thomas; Żuraw, Aleksandra

doi:10.1177/0192623320987804

Cited by 10 publications

(11 citation statements)

References 8 publications

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“…In a related study, Bédard et al published a proof of concept application of CNNs for the microscopic scoring of acute inflammation in DSS colitis [ 33 ]. They utilized a commercial artificial intelligence platform to detect muscle, normal mucosa, and acutely inflamed mucosa in H&E-stained murine colon WSIs.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based approach to the characterization and quantification of histopathology in mouse models of colitis

Kobayashi

Shieh²,

Sabando³

et al. 2022

PLoS ONE

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Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. Researchers have thus studied mouse models of colitis to further understand pathogenesis and identify new treatment targets. Flow cytometry and RNA-sequencing can phenotype immune populations with single-cell resolution but provide no spatial context. Spatial context may be particularly important in colitis mouse models, due to the simultaneous presence of colonic regions that are involved or uninvolved with disease. These regions can be identified on hematoxylin and eosin (H&E)-stained colonic tissue slides based on the presence of abnormal or normal histology. However, detection of such regions requires expert interpretation by pathologists. This can be a tedious process that may be difficult to perform consistently across experiments. To this end, we trained a deep learning model to detect ‘Involved’ and ‘Uninvolved’ regions from H&E-stained colonic tissue slides. Our model was trained on specimens from controls and three mouse models of colitis–the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5ΔIND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. Image patches predicted to be ‘Involved’ and ‘Uninvolved’ were extracted across mice to cluster and identify histological classes. We quantified the proportion of ‘Uninvolved’ patches and ‘Involved’ patch classes in murine swiss-rolled colons. Furthermore, we trained linear determinant analysis classifiers on these patch proportions to predict mouse model and clinical score bins in a prospectively treated cohort of mice. Such a pipeline has the potential to reveal histological links and improve synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based approach to the characterization and quantification of histopathology in mouse models of colitis

Kobayashi

Shieh²,

Sabando³

et al. 2022

PLoS ONE

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show abstract

“…In a related study, Bédard et al published a proof of concept application of convolutional neural networks for the microscopic scoring of acute inflammation in DSS colitis (32). They utilized a commercial artificial intelligence platform to detect muscle, normal mucosa, and acutely inflamed mucosa in H&E-stained murine colon WSIs.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based approach for the characterization and quantification of histopathology in mouse models of colitis

Kobayashi

Shieh

Sabando

et al. 2022

Preprint

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Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. As such, researchers have studied mouse models of colitis to further understand its pathogenesis and identify new treatment targets. Although bench methods like flow cytometry and RNA-sequencing can characterize immune responses with single-cell resolution, whole murine colon specimens are processed at once. Given the simultaneous presence of colonic regions that are involved or uninvolved with abnormal histology, processing whole colons may lead to a loss of spatial context. Detecting these regions in hematoxylin and eosin (H&E)-stained colonic tissues offers the downstream potential of quantifying immune populations in areas with and without disease involvement by immunohistochemistry on serially sectioned slides. This could provide a complementary, spatially-aware approach to further characterize populations identified by other methods. However, detection of such regions requires expert interpretation by pathologists and is a tedious process that may be difficult to perform consistently across experiments. To this end, we have trained a deep learning model to detect ‘Involved’ and ‘Uninvolved’ regions from H&E-stained colonic slides across controls and three mouse models of colitis – the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5ΔIND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. The trained classifier allows for extraction of ‘Involved’ colonic regions across mice to cluster and identify histological classes. Here, we show that quantification of ‘Involved’ and ‘Uninvolved’ image patch classes in swiss rolls of colonic specimens can be utilized to train a linear determinant analysis classifier to distinguish between mouse models. Such an approach has the potential for revealing histological links and improving synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms.

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“…Algorithm training and validation were performed with aiforia v4.6, as previously published. 18 , 19 The error against annotated training data was used as an evaluation metric for each CNN separately. The loss function for semantic segmentation networks was multiclass logistic regression.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a supervised deep learning algorithm for automated whole‐slide programmed death‐ligand 1 tumour proportion score assessment in non‐small cell lung cancer

et al. 2021

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Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer Aims: Immunohistochemical programmed deathligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning-based PD-L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD-L1 (22C3, laboratory-developed test)-stained samples. Methods and results: We designed a fully supervised deep learning algorithm for whole-slide PD-L1 assessment, consisting of four sequential convolutional neural networks (CNNs), using AIFORIA CREATE software. We included 199 whole slide images (WSIs) of 'routine diagnostic' histology samples from stage IV NSCLC patients, and trained the algorithm by using a training set of 60 representative cases. We validated the algorithm by comparing the algorithm TPS with the reference score in a held-out validation set. The algorithm had similar concordance with the reference score (79%) as the pathologists had with one another (75%). The intraclass coefficient was 0.96 and Cohen's j coefficient was 0.69 for the algorithm. Around the 1% and 50% cutoff points, concordance was also similar between pathologists and the algorithm. Conclusions: We designed a new, deep learningbased PD-L1 TPS algorithm that is similarly able to assess PD-L1 expression in daily routine diagnostic cases as pathologists. Successful validation on routine diagnostic WSIs and detailed visual feedback show that this algorithm meets the requirements for functioning as a 'scoring assistant'.

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Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis

Cited by 10 publications

References 8 publications

Deep learning-based approach to the characterization and quantification of histopathology in mouse models of colitis

Deep learning-based approach to the characterization and quantification of histopathology in mouse models of colitis

Deep learning-based approach for the characterization and quantification of histopathology in mouse models of colitis

Development and validation of a supervised deep learning algorithm for automated whole‐slide programmed death‐ligand 1 tumour proportion score assessment in non‐small cell lung cancer

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