Enhancing Radiological Reporting in Head and Neck Cancer: Converting Free-Text CT Scan Reports to Structured Reports Using Large Language Models

• Published in: The Indian journal of radiology & imaging
• Main Authors: Gupta, Amit, Malhotra, Hema, Garg, Amit K., Rangarajan, Krithika
• Format: Journal Article
• Language: English
• Published: Thieme Medical and Scientific Publishers Pvt. Ltd 01-08-2024
• Subjects: GPT-4; head and neck cancer; large language models; structured reporting
• ISSN: 0971-3026; 1998-3808
• DOI: 10.1055/s-0044-1788589

Objective The aim of this study was to assess efficacy of large language models (LLMs) for converting free-text computed tomography (CT) scan reports of head and neck cancer (HNCa) patients into a structured format using a predefined template. Materials and Methods A retrospective study was conducted using 150 CT reports of HNCa patients. A comprehensive structured reporting template for HNCa CT scans was developed, and the Generative Pre-trained Transformer 4 (GPT-4) was initially used to convert 50 CT reports into a structured format using this template. The generated structured reports were then evaluated by a radiologist for instances of missing or misinterpreted information and any erroneous additional details added by GPT-4. Following this assessment, the template was refined for improved accuracy. This revised template was then used for conversion of 100 other HNCa CT reports into structured format using GPT-4. These reports were then reevaluated in the same manner. Results Initially, GPT-4 successfully converted all 50 free-text reports into structured reports. However, there were 10 places with missing information: tracheostomy tube (n = 3), noninclusion of involvement of sternocleidomastoid muscle (n = 2), extranodal tumor extension (n = 3), and contiguous involvement of the neck structures by nodal mass rather than the primary (n = 2). Few instances of nonsuspicious lung nodules were misinterpreted as metastases (n = 2). GPT-4 did not indicate any erroneous additional findings. Using the revised reporting template, GPT-4 converted all the 100 CT reports into a structured format with no repeated or additional mistakes. Conclusion LLMs can be used for structuring free-text radiology reports using plain language prompts and a simple yet comprehensive reporting template. Summary Statement Large language models can successfully and accurately convert conventional radiology reports for oncology scans into a structured format using a comprehensive predefined template and thus can enhance the utility and integration of these reports in routine clinical practice. Key Points