Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review

European Journal of Cancer, Journal Year: 2021, Volume and Issue: 155, P. 200 - 215

Published: Aug. 11, 2021

Language: Английский

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A whole-slide foundation model for digital pathology from real-world data

Nature, Journal Year: 2024, Volume and Issue: 630(8015), P. 181 - 188

Published: May 22, 2024

Abstract Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands image tiles 1–3 . Prior models have often resorted to subsampling small portion for each slide, thus missing the important slide-level context 4 Here we present Prov-GigaPath, whole-slide foundation model pretrained on 1.3 billion 256 × in 171,189 whole slides from Providence, large US health network comprising 28 cancer centres. The originated more than 30,000 patients covering 31 major tissue types. To pretrain propose GigaPath, novel vision transformer architecture pretraining slides. scale GigaPath learning with tiles, adapts newly developed LongNet 5 method digital pathology. evaluate construct benchmark 9 subtyping tasks and 17 pathomics tasks, using both Providence TCGA data 6 With large-scale ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance 25 out 26 significant improvement over second-best 18 tasks. We further demonstrate potential vision–language 7,8 by incorporating reports. In sum, is an open-weight that achieves various demonstrating importance real-world modelling.

Language: Английский

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Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Nature Medicine, Journal Year: 2023, Volume and Issue: 29(1), P. 135 - 146

Published: Jan. 1, 2023

Language: Английский

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Biomarkers for immunotherapy of hepatocellular carcinoma

Nature Reviews Clinical Oncology, Journal Year: 2023, Volume and Issue: 20(11), P. 780 - 798

Published: Sept. 19, 2023

Language: Английский

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Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Oct. 17, 2022

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed new approach based on 3D deep convolutional neural networks to accurately differentiate mild dementia from cognitive impairment cognitively normal individuals using structural MRIs. For comparison, built reference model the volumes thickness previously reported brain regions that are known be implicated progression. We validate both models an internal held-out cohort The Disease Neuroimaging Initiative (ADNI) external independent National Coordinating Center (NACC). deep-learning is accurate, achieved area-under-the-curve (AUC) 85.12 when distinguishing between subjects with either MCI or dementia. more challenging task detecting MCI, it achieves AUC 62.45. It also significantly faster than volume/thickness which need extracted beforehand. can used forecast progression: misclassified as having by were progress over time. An analysis features learned proposed shows relies wide range associated disease. These findings suggest automatically learn identify imaging biomarkers predictive disease, leverage them achieve accurate early detection

Language: Английский

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Artificial intelligence for digital and computational pathology

Nature Reviews Bioengineering, Journal Year: 2023, Volume and Issue: 1(12), P. 930 - 949

Published: Oct. 2, 2023

Language: Английский

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Artificial intelligence to identify genetic alterations in conventional histopathology

The Journal of Pathology, Journal Year: 2022, Volume and Issue: 257(4), P. 430 - 444

Published: March 28, 2022

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many types, a limited set tests is currently part standard diagnostic workflows. However, universal testing for all alterations, especially rare ones, by cost and availability assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict probability specific genetic directly from conventional hematoxylin eosin (H&E) tissue slides. Although these are less accurate than gold (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools reduce workload analyses. this systematic literature review, we summarize state art predicting H&E using AI. We found AI perform reasonably well across although few algorithms been broadly validated. addition, FGFR, IDH, PIK3CA, BRAF, TP53, DNA repair pathways predictable while other rarely investigated were only poorly predictable. Finally, discuss next steps implementation AI-based surrogate © 2022 The Authors. Journal Pathology published John Wiley & Sons Ltd behalf Pathological Society Great Britain Ireland.

Language: Английский

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Application of Artificial Intelligence in Lung Cancer

Cancers, Journal Year: 2022, Volume and Issue: 14(6), P. 1370 - 1370

Published: March 8, 2022

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) good handling large volume computational repeated labor work suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts apply AI screening via CXR chest CT since 1960s. Several grand challenges were held find best model. Currently, FDA approved several programs reading, which enables systems take part detection. Following success application radiology field, was applied digitalized whole slide imaging (WSI) annotation. Integrating with more information, demographics clinical data, could play role decision-making by classifying EGFR mutations PD-L1 expression. also help clinicians estimate patient's prognosis predicting drug response, tumor recurrence rate after surgery, radiotherapy side effects. Though there are still some obstacles, deploying workflow vital foreseeable future.

Language: Английский

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Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application

ESMO Open, Journal Year: 2022, Volume and Issue: 7(2), P. 100400 - 100400

Published: March 3, 2022

Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly routine pathology slides, but the test performance has not been systematically investigated predefined thresholds.

Language: Английский

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Artificial intelligence in pancreatic cancer

Theranostics, Journal Year: 2022, Volume and Issue: 12(16), P. 6931 - 6954

Published: Jan. 1, 2022

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%.The pancreatic patients diagnosed early screening have median nearly ten years, compared 1.5 years for those not screening.Therefore, diagnosis and treatment are particularly critical.However, as rare general cost high, accuracy existing tumor markers enough, efficacy methods exact.In terms diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, other aspects, then lesions early.At same time, algorithm also be used to predict recurrence risk, metastasis, therapy response which could affect prognosis.In addition, widely in health records, estimating imaging parameters, developing computer-aided systems, etc. Advances AI applications will require concerted effort among clinicians, basic scientists, statisticians, engineers.Although it has some limitations, play an essential role overcoming foreseeable future due its mighty computing power.

Language: Английский

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