Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study

Bilal, Mohsin; Raza, Shan E. Ahmed; Azam, Ayesha; Graham, Simon; Ilyas, Mohammad; Cree, Ian A.; Snead, David; Minhas, Fayyaz; Rajpoot, Nasir M.

doi:10.1016/s2589-7500(21)00180-1

Cited by 169 publications

(186 citation statements)

References 38 publications

(114 reference statements)

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“…In this study, we have proposed a weakly supervised AI histology image pre-screening tool for CRC based on a variant of the IDaRS algorithm (15). The method presented in this study is aimed at assisting large bowel biopsy screening in clinical practice with a prior digital pre-screening.…”

Section: Discussionmentioning

confidence: 99%

“…For training a WSI classifier in a weakly supervised manner, we adapted our published method IDaRS (15), which works on the principle that all image tiles from the tissue regions in a WSI are not equally predictive of the WSI label. Therefore, instead of using all tiles, we choose two subsets of image tiles from each slide for the training.…”

Section: Methodsmentioning

confidence: 99%

“…They highlighted the need for larger datasets and the use of appropriate learning methodology to improve prediction accuracy. In our previous work, we proposed an iterative draw and rank sampling based weakly supervised WSI classification pipeline (IDaRS) for prediction of molecular pathways and genetic mutations in CRC (15). For the CRC pre-screening task in this study, we adapt IDaRS for the task of pre-screening of colon biopsies into normal and cancerous categories, termed as the IDaRS-COBI from hereon.…”

Section: Introductionmentioning

confidence: 99%

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AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images

Bilal

Tsang

Ali

et al. 2022

Preprint

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Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. To facilitate the diagnostic decision-making and reduce the workload of pathologists, we present an AI-based pre-screening tool capable of identifying normal and neoplastic colon biopsies. To learn the differential histological patterns from whole slides images (WSIs) stained with hematoxylin and eosin (H&E), our proposed weakly supervised deep learning method requires only slide-level labels and no detailed cell or region-level annotations. The proposed method was developed and validated on an internal cohort of biopsy slides (n=4 292) from two hospitals labeled with corresponding diagnostic categories assigned by pathologists after reviewing case reports. Performance of the proposed colon cancer pre-screening tool was evaluated in a cross-validation setting using the internal cohort (n=4 292) and also by an external validation on The Cancer Genome Atlas (TCGA) cohort (n=731). With overall cross-validated classification accuracy (AUROC = 0.9895) and external validation accuracy (AUROC = 0.9746), the proposed tool promises high accuracy to assist with the pre-screening of colorectal biopsies in clinical practice. Analysis of saliency maps confirms the representation of disease heterogeneity in model predictions and their association with relevant pathological features. The proposed AI tool correctly reported some slides as neoplastic while clinical reports suggested they were normal. Additionally, we analyzed genetic mutations and gene enrichment analysis of AI-generated neoplastic scores to gain further insight into the model predictions and explore the association between neoplastic histology and genetic heterogeneity through representative genes and signaling pathways.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images

Bilal

Tsang

Ali

et al. 2022

Preprint

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“…The study by Bilal and colleagues 8 underlines that computational models can accurately predict the status of molecular pathways and key mutations in colorectal cancer from standard histology sections. However, further questions need to be answered before purely image-based methods can be confidently used in clinical practice.…”

Section: Towards Computationally Efficient Prediction Of Molecular Signatures From Routine Histology Imagesmentioning

confidence: 99%

Towards computationally efficient prediction of molecular signatures from routine histology images

Lafarge

Koelzer

2021

The Lancet Digital Health

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“…Recently, deep learning methods have shown promising results for the prediction of the mutational status from digitized tissue stained with hematoxylin and eosin as whole slide images (WSI) (6)(7)(8)(9)(10)(11)(12). These WSI are already made routinely in the diagnostic workflow and deep learning methods are cheap, always feasible and very scalable.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images

Morel

Dérangère²,

Arnould́³

et al. 2022

Preprint

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The presence of mutation in cancer can be associated with a response to a targeted cancer therapy. Therefore it has become an important information while it helps giving a more specialized and more efficient treatment for each patient. Detection of mutation is routinely made by DNA sequencing diagnostic tests. Recently, a new strategy of deep learning based method of prediction of mutation from histopathological images has shown promising results. However it is still unknown whether these methods can be useful as screening tests aside from sequencing methods for efficient population diagnosis. Here we developed our own deep learning based prediction pipeline for the detection of mutation in breast, lung, colon and ovarian cancer and find clinically relevant genes that have easy to-detect mutational status, and especially TP53 which can be detected in these four tumors. We then propose 3 potential screening strategies and show the effectiveness of our deep learning pipeline for optimizing diagnosis in the patient population.

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Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study

Cited by 169 publications

References 38 publications

AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images

AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images

Towards computationally efficient prediction of molecular signatures from routine histology images

A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images

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