Semantic representation of reported measurements in radiology

Background In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. Methods We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. Results The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. Conclusions The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements

A systematic review of natural language processing applied to radiology reports

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 recent literature in NLP applied to radiology reports. Our automated literature search yields 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. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases 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. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but 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 providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication

Thyroid Ultrasound Report: A Position Statement of the Thyroid Study Group of the Portuguese Society of Endocrinology, Diabetes and Metabolism

A ecografi a tiroideia é o exame de imagem de primeira linha na investigação da patologia estrutural da tiroide. A utilização de sistemas de classifi cação do risco de malignidade dos nódulos da tiroide, em função das suas características ecográfi cas, veio conferir à ecografi a tiroideia um papel determinante na seleção dos nódulos com indicação para citologia aspirativa com agulha fi na da tiroide. Os relatórios da ecografi a tiroideia precisam de ser adaptados a esta realidade. Assim, a adoção de um modelo de relatório estruturado de ecografi a da tiroide, que inclua a utilização de um léxico comum e a informação sufi ciente para a classifi cação do risco dos nódulos da tiroide torna-se premente. Em Portugal, a qualidade dos relatórios de ecografi a da patologia nodular da tiroide, defi nida pela capacidade de classifi car corretamente o risco de malignidade dos nódulos, poderá ser baixa. Com o objetivo de contribuir para uma melhor qualidade dos relatórios de ecografi a da tiroide, o Grupo de Estudo da Tiroide da Sociedade Portuguesa de Endocrinologia, Diabetes e Metabolismo propôs-se estabelecer recomendações sobre a elaboração de um relatório estruturado da ecografi a tiroideia, tendo por base a classifi cação EU-TIRADS da European Thyroid Association, assim como sobre princípios e termos a utilizar na sua descrição.Thyroid ultrasound is the first-line imaging exam in the investigation of thyroid structural disease. The use of thyroid nodule malignancy risk classification systems according to their ultrasound characteristics has given the thyroid ultrasound a determining role in the selection of which nodules have indication for thyroid fine needle aspiration and cytology. Thyroid ultrasound reports need to be adapted to this reality. Thus, the adoption of a structured thyroid ultrasound report model that includes the use of a common lexicon and sufficient information to classify the risk of thyroid nodules becomes urgent. The quality of thyroid nodule ultrasound reports, defined by the ability to correctly classify the risk of nodule malignancy, may be low in Portugal. In order to contribute to an improvement in the quality of thyroid ultrasound reports, the Thyroid Study Group of the Portuguese Society of Endocrinology. Diabetes and Metabolism intended to establish recommendations on the elaboration of a structured report of thyroid ultrasound, based on the EU-TIRADS classification of the European Thyroid Association, as well as to establish principles and terminologies to be used in their implementation.These recommendations were developed within the Thyroid Study Group and were supported by the Portuguese Society of Endocrinology, Diabetes and Metabolism

A systematic review of natural language processing applied to radiology reports

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