Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports

Bulu, Hakan; Sippo, Dorothy A.; Lee, Janie M.; Burnside, Elizabeth S.; Rubin, Daniel L.

doi:10.1007/s10278-018-0064-0

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…Nonetheless, there remains an inherent risk of fragmentariness of the RadLex ontology owing to its top-down construction process by expert committees with experience in various radiological subdomains (Langlotz 2006) [4]. Fortunately, several attempts have been undertaken in the past to overcome this structural downside by means of automatic software based extraction of terms from different large-scale sources including a corpus of Pubmed repository articles as well as an enormous set of 270,540 free-text mammography reports (Bulu 2018, Hazen 2011 [10,20]. The latter study was performed with the aid of natural language processing, which could be an option to expand other radiology domains as well by using this complementary approach.…”

Section: Discussionmentioning

confidence: 99%

“…RadLex has demonstrated the most excellent results compared to other developed vocabularies in indexing radiological content collected from peer-reviewed biomedical publications, where nearly all images could be annotated with one or even multiple RadLex terms (Kahn 2014) [8]. Unfortunately, due to its manual top-down construction by experts in the eld, the RadLex ontology remains, by its very nature, incomplete with a large body of empirical literature revealing gaps of coverage in certain radiological elds such as mammography and chest computed tomography (Marwede 2008, Bulu 2018, Deshpande 2020 [9][10][11]. Focusing on the neurooncological domain of the neuroradiology subspecialty, to date there is no information on the applicability, performance and coverage of the RadLex vocabulary with respect to glioblastoma multiforme (GBM) magnetic resonance imaging (MRI) ndings, which are known to be the most common malignant primary brain tumors (World Health Organization classi cation of tumors of the central nervous system grade IV) of astrocytic origin with an increased incidence with age (Ostrom 2015) [12].…”

Section: Introductionmentioning

confidence: 99%

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Determining the applicability of the RSNA radiology lexicon (RadLex) in high-grade glioma MRI reporting – a preliminary study on 20 consecutive cases with newly diagnosed glioblastoma

Huckhagel

Stadelmann

Abboud

et al. 2021

Preprint

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Purpose The implementation of a collective terminology in radiological reporting such as the RSNA radiological lexicon (RadLex) yields many benefits including unambiguous communication of findings, improved education, and fostering data mining for research purposes. While some fields in general radiology have already been evaluated so far, this is the first exploratory approach to assess the applicability of the RadLex terminology for glioblastoma multiforme (GBM) MRI reporting. Methods Preoperative brain MRI reports of 20 consecutive patients with newly diagnosed GBM (mean age 68.4 ± 10.8 years; 12 males) between January and October 2010 were retrospectively identified. All terms related to the tumor as well as their frequencies of mention were extracted from the MRI reports by two independent neuroradiologists. Every item was subsequently analyzed with respect to an equivalent RadLex representation and classified into one of four groups as follows: 1. verbatim RadLex entity, 2. synonymous/multiple equivalent(s), 3. combination of RadLex concepts, or 4. no RadLex equivalent. Additionally, verbatim entities were categorized using the hierarchical RadLex Tree Browser. Results A total of 160 radiological terms were gathered. 123/160 (76.9%) items showed literal RadLex equivalents, 9/160 (5.6%) items had synonymous (non-verbatim) or multiple counterparts, 21/160 (13.1%) items were represented by means of a combination of concepts, and 7/160 (4.4%) entities could not eventually be transferred adequately into the RadLex ontology. Conclusions Our results suggest a sufficient term coverage of the RadLex terminology for GBM MRI reporting. If applied extensively, it may improve communication of radiological findings and facilitate data mining for large-scale research purposes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determining the applicability of the RSNA radiology lexicon (RadLex) in high-grade glioma MRI reporting – a preliminary study on 20 consecutive cases with newly diagnosed glioblastoma

Huckhagel

Stadelmann

Abboud

et al. 2021

Preprint

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“…Other approaches looked to extend existing medical resources using a frequent phrases approach, e.g. [120]. Works also used the derived concepts and relations visualising these to support activities, such as report reading and report querying (e.g.…”

Section: Nlp Methods Featuresmentioning

confidence: 99%

A systematic review of natural language processing applied to radiology reports

Casey

Davidson

Poon

et al. 2021

BMC Med Inform Decis Mak

119

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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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“…Other approaches looked to extend existing medical resources using a frequent phrases approach, e.g. [Bulu et al, 2018]. Works also used the derived concepts and relations visualising these to support activities, such as report reading and report querying (e.g.…”

Section: Nlp Methods Featuresmentioning

confidence: 99%

A Systematic Review of Natural Language Processing Applied to Radiology Reports

Casey,

Davidson,

Poon

et al. 2021

Preprint

View full text Add to dashboard Cite

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 4,799 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

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Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports

Cited by 11 publications

References 8 publications

Determining the applicability of the RSNA radiology lexicon (RadLex) in high-grade glioma MRI reporting – a preliminary study on 20 consecutive cases with newly diagnosed glioblastoma

Determining the applicability of the RSNA radiology lexicon (RadLex) in high-grade glioma MRI reporting – a preliminary study on 20 consecutive cases with newly diagnosed glioblastoma

A systematic review of natural language processing applied to radiology reports

A Systematic Review of Natural Language Processing Applied to Radiology Reports

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