An SVM approach towards breast cancer classification from H&amp;E-stained histopathology images based on integrated features

Aswathy, M; Mani, Mohan Prasath

doi:10.1007/s11517-021-02403-0

Cited by 37 publications

(18 citation statements)

References 35 publications

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“…Computer-based image analysis was proposed decades ago as a useful strategy to address this problem [16]. Early studies used rule-based image analysis [2,17], or so-called 'classical' machine learning algorithms, including support vector machines [18,19] or random forest classifiers [20,21]. The performance of these image analysis algorithms was massively improved by the advent of deep learning (DL), in particular by deep convolutional neural networks.…”

Section: Computer-based Image Analysis In Histopathologymentioning

confidence: 99%

Artificial intelligence to identify genetic alterations in conventional histopathology

Cifci

Foersch

Kather

2022

The Journal of Pathology

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

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Section: Computer-based Image Analysis In Histopathologymentioning

confidence: 99%

Artificial intelligence to identify genetic alterations in conventional histopathology

Cifci

Foersch

Kather

2022

The Journal of Pathology

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“…There were also several methods for feature extraction. [16] proposed a breast cancer classification system that uses support vector machine (SVM) classifier based on integrated features (texture, geometrical, and color). Results showed that among different classifiers, SVM gave better results with a test accuracy of approximately 90%.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Support Vector Machines by Meta-Heuristic Approach for Differential Diagnosis and Classification of Oral Diseases

Soltani¹,

Jaouadi²

2022

Preprint

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“…Machine learning tools are used to detect key features from complex datasets. SVM is a machine learning tool that is widely used in disease research to build predictive models, and it is known to produce effective and predictable models [6][7][8][9]. The following are a few examples of efficient predictive models built using SVM: a study showing the application of an SVM-based approach to identify postmenopausal women with low bone density [10] and a gene signature associated with postmenopausal osteoporosis that was detected and validated using SVM [11].…”

Section: Introductionmentioning

confidence: 99%

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Ding

Zhi

et al. 2022

BioMed Research International

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Background. Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes. Methods. Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes. Results. GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset. Conclusion. Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

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An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features

Cited by 37 publications

References 35 publications

Artificial intelligence to identify genetic alterations in conventional histopathology

Artificial intelligence to identify genetic alterations in conventional histopathology

Optimization of Support Vector Machines by Meta-Heuristic Approach for Differential Diagnosis and Classification of Oral Diseases

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

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