Systematic review of natural language processing (NLP) applications in magnetic resonance imaging (MRI)

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Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Systematic review of natural language processing (NLP) applications in magnetic resonance imaging (MRI) Gazal Mahmeed , View ORCID Profile Dana Brin , Eli Konen , Girish N Nadkarni , Eyal Klang doi: https://doi.org/10.1101/2024.07.21.24310760 Gazal Mahmeed 1 Division of Diagnostic Imaging, Sheba Medical Center , Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University , Tel Aviv, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Gazal.k.m{at}outlook.com Dana Brin 1 Division of Diagnostic Imaging, Sheba Medical Center , Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University , Tel Aviv, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dana Brin Eli Konen 1 Division of Diagnostic Imaging, Sheba Medical Center , Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University , Tel Aviv, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Girish N Nadkarni 2 Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA 3 The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eyal Klang 2 Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA 3 The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background As MRI use grows in medical diagnostics, applying NLP techniques could improve management of related text data. This review aims to explore how NLP can augment radiological evaluations in MRI. Methods We conducted a PubMed search for studies that applied NLP in the clinical analysis of MRI, including publications up to January 4, 2024. The quality and potential bias of the included studies were assessed using the QUADAS-2 tool. Results Twenty-six studies published between April 2010 and January 2024, covering more than 160k MRI reports were analyzed. Most of these studies demonstrated low to no risk of bias of the NLP. Neurology was the most frequently studied specialty, with twelve studies, followed by musculoskeletal (MSK) and body imaging. Applications of NLP included staging, quantification, and disease diagnosis. Notably, NLP showed high precision in tumor staging classification and structuring of free-text reports. Conclusion NLP shows promise in enhancing the utility of MRI. However, there is a need for prospective studies to further validate NLP algorithms in real-time clinical and operational scenarios and across various radiology specialties, which could lead to broader applications in healthcare. Introduction Natural language processing (NLP) combines computer science, artificial intelligence, and linguistics to improve how computers and humans interact. The introduction of technologies like ChatGPT in 2022 marked a major shift in NLP, showcasing its broad potential 1 . In radiology, traditionally reliant on computer vision 2 , 3 NLP introduces a new angle 4 , 5 , with many potential uses, including flagging findings, prioritizing patients, generating imaging protocols, and conducting research 6 , 7 . MRI, known for its high contrast resolution and no radiation, is becoming more prevalent in diagnostic practices 8 . Using NLP in MRI interpretation could enhance workflows, diagnostics, and patient care. This review assesses how NLP improves MRI applications by enhancing textual analysis in radiology. Methods This systematic review was reported according to the preferred reporting items for systematic reviews guidelines (PRISMA). The study is registered under PROSPERO, number (CRD42024518710). Search strategy We searched literature to find studies on NLP’s clinical uses in MRI. The search was conducted on January 4, 2024, using the PubMed database. Search keywords included “MRI”, “Magnetic resonance imaging”, “MRE”, “Magnetic resonance enterography”, “ NLP”, “Natural Language Processing“, “LLM”, “large language models”, and “chatGPT”. Details on complete search strategies are provided in ( Supplementary Material ) . Inclusion criteria were studies that (1) evaluated the clinical applications of NLP for MRI, (2) original articles in english (3) articles exclusively pertaining to MRI imaging. We excluded (1) non - available full-text articles, (2) written in language other than english, (3) studies that included various imaging modalities other than MRI, (4) not focusing on NLP techniques, (5) studies focused on image processing and interpretation rather than text-based data analysis. Study selection Two reviewers (GM, DB) independently screened the titles and abstracts to determine whether the studies met the inclusion criteria. The full-text article was reviewed when the title met the inclusion criteria or when there was any uncertainty. Disagreements were adjudicated by a third reviewer (EK). Data extraction We used a standardized sheet to collect data on publication year, study design, location, database size, criteria, NLP methods, radiology field, MRI technique, NLP usage in MRI context, and performance. Quality assessment and risk of bias Quality was assessed by the adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria 9 . Details on quality assessment are provided in ( Supplementary Material ) . Data synthesis and analysis The analysis in this review is mainly qualitative. The heterogeneity of the studies in the literature evaluating NLP in MRI, their methods and the reported results precludes us from performing a meta-analysis. Results Study selection and characteristics The initial search yielded 823 articles, with 26 meeting our inclusion criteria. Figure 1 summarizes the characteristics of the included studies. The studies were published between 2010 and 2024. Table 1 lists the publications reporting on the use of NLP in MRI. Download figure Open in new tab Figure 1: PRISMA flowchart of the study selection process. View this table: View inline View popup Table 1: Publications reporting on the use of NLP in MRI. Descriptive summary of results Over the past years there has been an increase in the number of publications of NLP applied to MRI. Figure 2 represents this general trend, with a peak of publications in 2023. All studies included were retrospective in nature, encompassing 163,209 MRI reports. Download figure Open in new tab Figure 2: Evaluation of NLP applications in MRI research: trend analysis over time. Note that the studies included were published before January 2024. Figure 3 presents the distribution of radiology fields in the study. Neurology was the most prominent field, accounting for 12 out of 26 of the reviewed studies 11 14 16 17 18 22 23 24 27 30 31 34 . Musculoskeletal 13 , 25 26 32 and body 19 20 28 35 imaging were studied in four publications each, making them the second most represented fields. Cardiac imaging followed with three research studies 10 15 29 , while two studies involved breast imaging 12 21 . Additionally, there was one study that discussed the use of NLP for MRI in a more general context, without focusing on a specific field 33 . Download figure Open in new tab Figure 3: Radiology Fields Distribution Percentages The studies included various NLP techniques, such as rule based approaches, machine learning, and deep learning, including large language models (LLMs). The complete list of NLP techniques is detailed in Table 2 . View this table: View inline View popup Table 2: Summary of Studies Applying NLP to MRI: Modalities, Methods, Models, Tasks, and Performance Scores. We identified numerous valuable clinical applications of NLP in MRI. These applications are summarized in Figure 4 , The key findings from each study are extensively described in Table 3 . Download figure Open in new tab Figure 4: Natural language processing (NLP) MRI applications. View this table: View inline View popup Table 3: Summary of Main Results and Limitations of Synthesized Studies. NLP was found to have diverse applications in research studies, predominantly in staging, quantification, disease diagnosis, and protocol selection. Seven studies have focused on employing NLP for staging and quantification . Lee SJ et al. 16 demonstrated the accurate interpretation of BT-RADS report scores through NLP trained on structured reports, while Zhang D et al. 20 utilized a rule-based algorithm to automatically categorize prostate MRI reports. Six studies utilized NLP for disease diagnosis . Notably, in the studies by Dewaswala N et al. 10 and Liu Y et al. 12 , NLP effectively extracted diagnoses like hypertrophic cardiomyopathy and information about index lesions from breast MRI reports, respectively. Moreover, NLP was integrated into four studies for protocol selection , exemplified by Trivedi H et al. 25 study, which employed natural language classification to identify contrast requirements in musculoskeletal MRIs. In the realm of prognosis prediction , Kim S et al. 28 introduced a novel computer model to analyze MRI reports of rectal cancer patients for estimating their survival times. Additionally, Valtchiney VI et al. 33 explored methods for identifying MRI safety risks related to implanted devices in a study focusing on safety protocol compliance, while Chung EM et al. 35 not only developed a logistic regression model to predict patients requiring spine surgery but also investigated the use of ChatGPT technology to succinctly summarize MRI reports and enhance patient comprehension. Discussion Our review showcased the growing importance of NLP in MRI, with 26 publications covering over 160k MRI reports, across several organ systems. However, a key weakness is that only one prospective study was conducted 27 , such studies are essential for validating NLP algorithms in real-time clinical and operational settings to bolster clinical decision-making, workflow efficiency, and personalized medicine approaches. Future research should focus more on prospective studies to better validate the real-world benefits. Additionally, recommendations include expanding NLP applications to other radiology fields such as pediatric radiology, and particularly in the context of cancer diagnosis and staging to broaden the impact and potential utility of NLP in the field. The results of our review highlight several significant opportunities to streamline MRI imaging processes using advanced technology. By implementing more sophisticated protocols, bolstered by NLP, we could potentially reduce the time required for approving referrals 26 . This simplification means a more efficient workflow for healthcare providers. Additionally, our findings suggest that NLP can simplify interpretative data for patients. This transparency allows patients to better understand their health information, which can improve their engagement and satisfaction with the treatment process 35 . For radiologists, the technology we studied offers support in summarizing complex imaging results, such as those from MRI scans. This aid not only speeds up their workflow but also enhances the accuracy and comprehensiveness of the diagnostic data provided, especially in MRI reports with structured templates 19 . Looking ahead, the possibilities for further integration of this technology across different imaging domains are vast. Each specialty can learn from the others, leveraging technological advancements for the benefit of patient care and system efficiency. Our review has limitations. First, we did not perform a meta-analysis due to the high heterogeneity in the methodologies and tasks used across the referenced studies, which made direct comparisons problematic. Second, our review was limited to articles sourced from PubMed, potentially omitting relevant studies published in other databases. Furthermore, the scope of our analysis was restricted to English-language publications, excluding potentially significant research available in other languages. Lastly, our findings are confined to the data available up to the point of our review, and as such, newer studies post-review are not considered. In conclusion, NLP applications in MRI show potential for a change in the field. However, while the review revealed a wealth of evidence supporting the effectiveness of NLP in MRI analyses, the presence of just one prospective studies underscores the need for further research to validate NLP algorithms in real-time clinical and operational settings. Moreover, expanding the scope of NLP usage to encompass other radiology specialties, presents opportunities for advancing healthcare practices. By addressing these recommendations in future research endeavors, the integration of NLP technologies stands to enhance clinical decision support, drive research advancements, and improve patient outcomes in the realm of MRI imaging. 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Share Systematic review of natural language processing (NLP) applications in magnetic resonance imaging (MRI) Gazal Mahmeed , Dana Brin , Eli Konen , Girish N Nadkarni , Eyal Klang medRxiv 2024.07.21.24310760; doi: https://doi.org/10.1101/2024.07.21.24310760 Share This Article: Copy Citation Tools Systematic review of natural language processing (NLP) applications in magnetic resonance imaging (MRI) Gazal Mahmeed , Dana Brin , Eli Konen , Girish N Nadkarni , Eyal Klang medRxiv 2024.07.21.24310760; doi: https://doi.org/10.1101/2024.07.21.24310760 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Radiology and Imaging Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (302) Cardiovascular Medicine (4453) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15242) Forensic Medicine (30) Gastroenterology (1131) Genetic and Genomic Medicine (6615) Geriatric Medicine (669) Health Economics (1001) Health Informatics (4552) Health Policy (1372) Health Systems and Quality Improvement (1614) Hematology (543) HIV/AIDS (1270) Infectious Diseases (except HIV/AIDS) (15929) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6625) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3344) Ophthalmology (979) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5461) Public and Global Health (9252) Radiology and Imaging (2207) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a02c36da3f4e1640',t:'MTc3OTk2MDM0OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();