Improving Our Understanding of Indolent Lesions: A New Role for AI.

Y and coauthors have applied natural language processing (NLP) methods to identify abdominal CT and MRI reports that indicated a pancreatic cystic lesion (PCL) was present and then automatically extract the measurements of the identified lesions (1). The results were compared against expert radiologists who reviewed a set of 1000 reports to annotate them regarding the presence or absence of a PCL and the measurements of the cystic lesion(s), if present. In further analysis, follow-up studies among patients with PCLs were evaluated for any change in size of the lesions. The histologic findings of resected lesions were correlated with the size of the lesions and any increase or decrease in size that occurred between the initial and follow-up scans. As the authors note, PCLs present a clinical dilemma. Because PCLs are found as incidental findings in as many as 19.5% of abdominal MRI studies, the question remains as to how these lesions should be managed (2). The American College of Radiology (ACR) Incidental Findings Committee developed an algorithm for clinical management (3). The authors’ development of an NLP system to identify radiology reports (and hence, the studies that generated them) and extract measurements provides for a potential way to accumulate a large database of PCLs and then mine that database to improve understanding of the natural history of PCLs. One finding of the authors’ study took advantage of reports that had been generated prior to the ACR white paper on the management of incidental pancreatic cysts and compared the measurements with reports generated after the publication of the white paper. Their algorithms achieved higher performance in terms of agreement with the radiologists on reports generated after the white paper publication (table 3 of the authors’ article), likely because the radiologists began using the recommended measurement methods described in the white paper and so were more uniform in the reports than when measurements were ad hoc. Overall, the NLP methods the authors used showed a very high degree of agreement with the radiologists who were used as the reference standard for the identification and measurements of PCLs in the reports. The authors also have used publicly available software and made available their modifications and models using the software. Part of their objective was to provide other researchers with the methods used in their article to encourage expansion of their study or application to other NLP tasks. Although the article is primarily a successful demonstration of the use of NLP for a clinically challenging task, the potential for the use of NLP in radiology is extensive, and some aspects are likely already in wide use. A review of NLP in radiology by Pons et al provides both an overview of NLP methods and a list of NLP applications in use at the time of the publication (4). Examples include a system that extracts information from reports and automatically generates Breast Imaging Reporting and Data System (ie, BI-RADS) final assessment categories; a system for automatic extraction of information from clinical free text in electronic medical records; a commercial automated system for coding radiology reports for billing and revenue management; and a system that extracts, structures, and classifies unstructured radiology reports, among many others. The latter example, classifying unstructured radiology reports, has potential economic impact for radiology. The ideas behind structured reporting have been discussed among academic and nonacademic radiologists for years. While benefits have been touted, there are also studies that have shown that structured reporting, in some instances, decreased radiology report accuracy and completeness (5). The Johnson et al article, however, has weaknesses because the reporting system used was then new (in beta testing at the time) and the residents had limited training time using it. In a more recent review, Ganeshan and colleagues provide a comprehensive review of the importance of Improving Our Understanding of Indolent Lesions: A New Role for AI