research-article

A Prediction Model of Microsatellite Status from Histology Images

Authors:

Jason D. Wright,

Xiaoyao LiangAuthors Info & Claims

ICBET '20: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology

Pages 334 - 338

https://doi.org/10.1145/3397391.3397442

Published: 15 September 2020 Publication History

Abstract

Machine learning approaches have received sufficient attention in tumor detection in histopathology, and the very recent researches show their potential in extraction of molecular information for biomarker prediction. However, as we can only obtain label information of the whole slide, the patch-wise prediction classification results are simply summarized to reach the final diagnosis in previous work. In this paper, we develop a novel framework to precisely predict biomarker from hematoxylin and eosin (H&E) stained histology slides, where microsatellite instability (MSI) status in colorectal cancer is used as a case study. We develop a patch-wise binary classifier to detect tumor tissue as biomarker is tightly associated with tumor tissues. To obtain a precise predication of MSI status, a noise-robust convolutional neural network is trained by relabeling patch-wise output iteratively and feed back as input information. We employ a distillation framework for the dataset relabeling task. We also design a mathematical algorithm to sort out representative patches towards the final MSI status prediction. The model is evaluated by a large patient cohort from The Cancer Genome Atlas (TCGA), and tested on the state-of-the-art deep learning device NVIDIA GPU TeslaTM V100. The experimental results demonstrate the improved reliability in MSI prediction from histology images.

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Cited By

Zhang HXu XWang GShen Y(2024)TPC-GNN: A Three-Level Hierarchical Graph Neural Network for Microsatellite Instability Prediction from Histopathology Whole Slide Images2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821910(2880-2887)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10821910
Shen YSowmya ALuo YLiang XShen DKe J(2023)A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GANIEEE Transactions on Medical Imaging10.1109/TMI.2022.322172442:7(1969-1981)Online publication date: Jul-2023
https://doi.org/10.1109/TMI.2022.3221724
Alam MAbdul-Ghafar JYim KThakur NLee SJang HJung CChong Y(2022)Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic ReviewCancers10.3390/cancers1411259014:11(2590)Online publication date: 24-May-2022
https://doi.org/10.3390/cancers14112590
Show More Cited By

Index Terms

A Prediction Model of Microsatellite Status from Histology Images
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging

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ICBET '20: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology

September 2020

350 pages

ISBN:9781450377249

DOI:10.1145/3397391

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 15 September 2020

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ICBET 2020

ICBET 2020: 2020 10th International Conference on Biomedical Engineering and Technology

September 15 - 18, 2020

Tokyo, Japan

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Cited By

Zhang HXu XWang GShen Y(2024)TPC-GNN: A Three-Level Hierarchical Graph Neural Network for Microsatellite Instability Prediction from Histopathology Whole Slide Images2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821910(2880-2887)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10821910
Shen YSowmya ALuo YLiang XShen DKe J(2023)A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GANIEEE Transactions on Medical Imaging10.1109/TMI.2022.322172442:7(1969-1981)Online publication date: Jul-2023
https://doi.org/10.1109/TMI.2022.3221724
Alam MAbdul-Ghafar JYim KThakur NLee SJang HJung CChong Y(2022)Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic ReviewCancers10.3390/cancers1411259014:11(2590)Online publication date: 24-May-2022
https://doi.org/10.3390/cancers14112590
Shen YShen DKe J(2022)Identify Representative Samples by Conditional Random Field of Cancer Histology ImagesIEEE Transactions on Medical Imaging10.1109/TMI.2022.319852641:12(3835-3848)Online publication date: Dec-2022
https://doi.org/10.1109/TMI.2022.3198526
Shen YKe J(2022)Sampling Based Tumor Recognition in Whole-Slide Histology Image With Deep Learning ApproachesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2021.306223019:4(2431-2441)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TCBB.2021.3062230
Murchan PÓ’Brien CO’Connell SMcNevin CBaird ASheils OÓ Broin PFinn S(2021)Deep Learning of Histopathological Features for the Prediction of Tumour Molecular GeneticsDiagnostics10.3390/diagnostics1108140611:8(1406)Online publication date: 3-Aug-2021
https://doi.org/10.3390/diagnostics11081406
Shen YKe J(2021)Representative Region Based Active Learning For Histological Classification Of Colorectal Cancer2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)10.1109/ISBI48211.2021.9433931(1730-1733)Online publication date: 13-Apr-2021
https://doi.org/10.1109/ISBI48211.2021.9433931
Shen YKe J(2021)Su-Sampling Based Active Learning For Large-Scale Histopathology Image2021 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP42928.2021.9506262(116-120)Online publication date: 19-Sep-2021
https://doi.org/10.1109/ICIP42928.2021.9506262

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