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Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

ABSTRACT: Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.

SUBMITTER: Ahmed Z

PROVIDER: S-EPMC4990152 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

Ahmed Zeeshan Z Zeeshan Saman S Dandekar Thomas T

Database : the journal of biological databases and curation 20160818

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effec ...[more]

PMID: 27538578

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Project description:BackgroundFor selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.MethodsA biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.ResultsThe current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html.ConclusionsThe approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Project description:BackgroundVarious features extracted from breast MRI have the potential to serve as noninvasive biomarkers for the prediction of the biologic behavior of breast cancer. The purpose of this study was to investigate the value of focal breast edema and breast edema score (BES) on T2-weighted images in providing valuable biological information for breast cancer patients' personalized treatment.MethodTwo hundred and five lesions in 201 patients with invasive breast cancer confirmed by surgery or biopsy in Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine from November 2018 to October 2019 were retrospectively recruited and analyzed in this study. Focal edema and BES were evaluated at fat-suppressed T2 weighted imaging. All the lesions were divided into two groups according to the presence of focal edema. The differences in clinicopathological characteristics between the two groups and between different BES were compared.ResultsTwo hundred and five lesions in 201 patients with invasive breast cancer were retrospectively recruited and analyzed in this study. On the fat-suppressed T2WI, focal edema was detected in 102 of 205 lesions (49.8%). BES was positively correlated with tumor size (p < 0.001), histologic grade (p = 0.006), Ki-67 index (p < 0.001), and N stage (p = 0.007), and was negatively correlated with expression of ER and PR (p < 0.001). Higher BES was more likely to present in patients with non-luminal breast cancer (p < 0.001) and suggested the possibility of a higher N stage.ConclusionsFocal edema on T2WI of breast MRI indicates stronger tumor invasiveness, in which non-luminal breast cancer is more inclined to present focal edema. Breast edema score, a novel and practical tool, helps guide the individualized treatment of patients with invasive breast cancer.Critical relevance statementFocal edema on T2WI of breast MRI indicates stronger tumor invasiveness. Breast edema score helps guide the individualized treatment of patients with invasive breast cancer.

Dataset Information

Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

Publications

Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

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