Journal Article

Utilizing a domain-specific large language model for LI-RADS v2018 categorization of free-text MRI reports: a feasibility study

Mauro, Ezequiel, Reig, María, Comas-Cufí, Marc, Saborido, Belén, ner, Alejandro, Rimola, Jordi, Pazó, Javier, Matute-González, Mario, Soler, Alexandre, Darnell, Anna, Turnes, Juan

2024

Overview

ObjectiveTo develop a domain-specific large language model (LLM) for LI-RADS v2018 categorization of hepatic observations based on free-text descriptions extracted from MRI reports.Material and methodsThis retrospective study included 291 small liver observations, divided into training (n = 141), validation (n = 30), and test (n = 120) datasets. Of these, 120 were fictitious, and 171 were extracted from 175 MRI reports from a single institution. The algorithm’s performance was compared to two independent radiologists and one hepatologist in a human replacement scenario, and considering two combined strategies (double reading with arbitration and triage). Agreement on LI-RADS category and dichotomic malignancy (LR-4, LR-5, and LR-M) were estimated using linear-weighted κ statistics and Cohen’s κ, respectively. Sensitivity and specificity for LR-5 were calculated. The consensus agreement of three other radiologists served as the ground truth.ResultsThe model showed moderate agreement against the ground truth for both LI-RADS categorization (κ = 0.54 [95% CI: 0.42–0.65]) and the dichotomized approach (κ = 0.58 [95% CI: 0.42–0.73]). Sensitivity and specificity for LR-5 were 0.76 (95% CI: 0.69–0.86) and 0.96 (95% CI: 0.91–1.00), respectively. When the chatbot was used as a triage tool, performance improved for LI-RADS categorization (κ = 0.86/0.87 for the two independent radiologists and κ = 0.76 for the hepatologist), dichotomized malignancy (κ = 0.94/0.91 and κ = 0.87) and LR-5 identification (1.00/0.98 and 0.85 sensitivity, 0.96/0.92 and 0.92 specificity), with no statistical significance compared to the human readers’ individual performance. Through this strategy, the workload decreased by 45%.ConclusionLI-RADS v2018 categorization from unlabelled MRI reports is feasible using our LLM, and it enhances the efficiency of data curation.Critical relevance statementOur proof-of-concept study provides novel insights into the potential applications of LLMs, offering a real-world example of how these tools could be integrated into a local workflow to optimize data curation for research purposes.Key PointsAutomatic LI-RADS categorization from free-text reports would be beneficial to workflow and data mining.LiverAI, a GPT-4-based model, supported various strategies improving data curation efficiency by up to 60%.LLMs can integrate into workflows, significantly reducing radiologists’ workload.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Arbitration / Classification / Data mining / Feasibility studies / Human performance / Large language models / Sensitivity / Workflow / Workload / Workloads