Advancements in Standardizing Radiological Reports: A Comprehensive Review

Abstract

Standardized radiological reports stimulate debate in the medical imaging field. This review paper explores the advantages and challenges of standardized reporting. Standardized reporting can offer improved clarity and efficiency of communication among radiologists and the multidisciplinary team. However, challenges include limited flexibility, initially increased time and effort, and potential user experience issues. The efforts toward standardization are examined, encompassing the establishment of reporting templates, use of common imaging lexicons, and integration of clinical decision support tools. Recent technological advancements, including multimedia-enhanced reporting and AI-driven solutions, are discussed for their potential to improve the standardization process. Organizations such as the ACR, ESUR, RSNA, and ESR have developed standardized reporting systems, templates, and platforms to promote uniformity and collaboration. However, challenges remain in terms of workflow adjustments, language and format variability, and the need for validation. The review concludes by presenting a set of ten essential rules for creating standardized radiology reports, emphasizing clarity, consistency, and adherence to structured formats.

Keywords: AI; radiology; radiology report; radiomics; standardization; structured report.

Figure 1

A standardized radiological report template developed at the University Hospital IEO (European Institute of Oncology) for a reporting an imaging-guided vacuum-assisted breast biopsy. The template consists of clearly defined sections, including clinical information, imaging techniques, type of procedure, needle details, and lesion description. The radiologist is guided through the reporting process with multiple-choice options, ensuring consistency in data input. The resulting report contains reproducible terms and can be easily extracted and classified by software for subsequent radiomics and AI analysis.

Figure 2

The process to structure unstructured reports. Natural language processing (NLP) techniques extract relevant information from the report, including patient demographics, clinical history, imaging findings, and impressions. The extracted details are then organized using standardized reporting formats, promoting consistency and uniformity in the reports.