What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing.

Autor: Crombé A; IMADIS, 48 rue quivogne, 63002, Lyon, France. a.crombe@imadis.fr.; University of Bordeaux, 33000, Bordeaux, France. a.crombe@imadis.fr., Seux M; IMADIS, 48 rue quivogne, 63002, Lyon, France., Bratan F; IMADIS, 48 rue quivogne, 63002, Lyon, France.; Department of Diagnostic and Interventional Imaging, Centre Hospitalier Saint-Joseph Saint-Luc, 69007, Lyon, France., Bergerot JF; IMADIS, 48 rue quivogne, 63002, Lyon, France.; Ramsay Générale de Santé, Clinique Convert, 01000, Bourg-en-Bresse, France., Banaste N; IMADIS, 48 rue quivogne, 63002, Lyon, France.; Department of Radiology, Hôpital Nord-Ouest, 69400, Villefranche-sur-Saône, France., Thomson V; IMADIS, 48 rue quivogne, 63002, Lyon, France.; Ramsay Générale de Santé, Clinique de la Sauvegarde, 69009, Lyon, France., Lecomte JC; IMADIS, 48 rue quivogne, 63002, Lyon, France.; Centre Hospitalier de Saintonge, 17100, Saintes, France.; Centre Aquitain d'Imagerie, 33600, Pessac, France., Gorincour G; IMADIS, 48 rue quivogne, 63002, Lyon, France.; ELSAN, Clinique Bouchard, 13006, Marseille, France.
Jazyk: angličtina
Zdroj: Journal of digital imaging [J Digit Imaging] 2022 Aug; Vol. 35 (4), pp. 993-1007. Date of Electronic Publication: 2022 Mar 22.
DOI: 10.1007/s10278-022-00619-6
Abstrakt: Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with natural language processing for epidemiological purposes, activity monitoring or data collection. These reports are obtained under various on-call conditions by radiologists with various backgrounds. Our aim was to investigate what influences the radiologists' written expressions. To do so, this retrospective multicentric study included 30,227 emergency radiological reports of computed tomography scans and magnetic resonance imaging involving exactly one body region, only with pathological findings, interpreted from 2019-09-01 to 2020-02-28 by 165 radiologists. After text pre-processing, one-word tokenization and use of dictionaries for stop words, polarity, sentiment and uncertainty, 11 variables depicting the structure and content of words and sentences in the reports were extracted and summarized to 3 principal components capturing 93.7% of the dataset variance. In multivariate analysis, the 1 st principal component summarized the length and lexical diversity of the reports and was significantly influenced by the weekday, time slot, workload, number of examinations previously interpreted by the radiologist during the on-call period, type of examination, emergency level and radiologists' gender (P value range: < 0.0001-0.0029). The 2 nd principal component summarized negative formulations, polarity and sentence length and was correlated with the number of examination previously interpreted by the radiologist, type of examination, emergency level, imaging modality and radiologists' experience (P value range: < 0.0001-0.0032). The last principal component summarized questioning, uncertainty and polarity and was correlated with the type of examination and emergency level (all P values < 0.0001). Thus, the length, structure and content of emergency radiological reports were significantly influenced by organizational, radiologist- and examination-related characteristics, highlighting the subjectivity and variability in the way radiologists express themselves during their clinical activity. These findings advocate for more homogeneous practices in radiological reporting and stress the need to consider these influential features when developing models based on natural language processing.
(© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
Databáze: MEDLINE
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