Classifying tumor event attributes in radiology reports

Yim, Wen-wai; Kwan, Sharon W.; Yetisgen, Meliha

doi:10.1002/asi.23937

Cited by 19 publications

(15 citation statements)

References 18 publications

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“…Naïve Bayes was the second most frequent approach, being used to predict heart disease in medical records [30,80], classify smoking status [52], search EMR records to identify multiple sclerosis [106], and classify EMR records for obesity [86] and cancer [60,65,67]. CRFs are the third most frequent approach, have been used to predict heart disease in medical records [29,32], identify EHR progress notes pertaining to diabetes [85], categorize breast radiology reports [22], and identify tumor attributes in radiology reports [63]. Lastly, random forests were used for predicting heart disease [53], classifying cancer types [60], and identifying hypertension [49].…”

Section: Resultsmentioning

confidence: 99%

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

359

259

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Background Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. Objective The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and their variations as keywords to maximize coverage of the articles. Results Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision . The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods...

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Section: Resultsmentioning

confidence: 99%

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

359

259

View full text Add to dashboard Cite

show abstract

“…Apoptosis/necrosis, as well as micronuclei formation, were also investigated as common endpoints during radiobiological studies. As an experimental model, the MRC-5 cell line was used, which represents well-characterized human normal fibroblasts approved for radiobiological research [15] and is an appropriate model system for studying early and late radiation effects [16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Laser-Driven Ultrashort Pulsed Electron Beam Radiation at Doses of 0.5 and 1.0 Gy Induces Apoptosis in Human Fibroblasts

Babayan

Grigoryan

Khondkaryan

et al. 2019

IJMS

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Rapidly evolving laser technologies have led to the development of laser-generated particle accelerators as an alternative to conventional facilities. However, the radiobiological characteristics need to be determined to enhance their applications in biology and medicine. In this study, the radiobiological effects of ultrashort pulsed electron beam (UPEB) and X-ray radiation in human lung fibroblasts (MRC-5 cell line) exposed to doses of 0.1, 0.5, and 1 Gy are compared. The changes of γH2AX foci number as a marker of DNA double-strand breaks (DSBs) were analyzed. In addition, the micronuclei induction and cell death via apoptosis were studied. We found that the biological action of UPEB-radiation compared to X-rays was characterized by significantly slower γH2AX foci elimination (with a dose of 1 Gy) and strong apoptosis induction (with doses of 0.5 and 1.0 Gy), accompanied by a slight increase in micronuclei formation (dose of 1 Gy). Our data suggest that UPEB radiation produces more complex DNA damage than X-ray radiation, leading to cell death rather than cytogenetic disturbance.

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“…e.g., [28][29][30][31][32][33]. Studies focused on tumour information e.g., for liver [34] and hepatocellular carcinoma (HPC) [35,36] and one study on extracting information relevant for structuring subdural haematoma characteristics in reports [37].…”

Section: Diagnostic Surveillancementioning

confidence: 99%

A systematic review of natural language processing applied to radiology reports

Casey

Davidson

Poon

et al. 2021

BMC Med Inform Decis Mak

101

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Background Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports. Methods We conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. Results We present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Conclusions Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

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Classifying tumor event attributes in radiology reports

Cited by 19 publications

References 18 publications

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Laser-Driven Ultrashort Pulsed Electron Beam Radiation at Doses of 0.5 and 1.0 Gy Induces Apoptosis in Human Fibroblasts

A systematic review of natural language processing applied to radiology reports

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