Application of large language models for assigning clear cell likelihood score v2.0 from free-text MRI reports: a feasibility study.
Abstract
Rationale
AND
Objectives
To evaluate the performance of four large language models (LLMs) for automated feature extraction and clear cell likelihood score version 2.0 (ccLS v2.0) assignment from free-text reports using a multi-step prompting strategy.
Materials And Methods
This retrospective multicenter study included 1,048 magnetic resonance imaging (MRI) reports of renal masses from three institutions (2020-2026). Four LLMs (DeepSeek-V3.2, Qwen 3.5, GPT-5.3, and Gemini 3.0) were evaluated for automated ccLS v2.0 assignment through a three-stage prompting workflow: (1) feature extraction, (2) rule-based criteria matching, and (3) final categorization. Performance was evaluated against expert radiologist consensus-based ccLS v2.0 assignments. Accordingly, these results primarily reflect agreement in task execution rather than diagnostic accuracy for ccRCC.
Results
Gemini 3.0 achieved the highest overall accuracy, correctly assigning ccLS categories in 683 of 802 reports in the internal cohort (85.2%; 95% CI, 82.5%-87.5%) and 211 of 246 reports in the external validation cohort (85.8%; 95% CI, 80.9%-89.6%). All LLMs achieved accuracy greater than 80% for assigning high-risk categories (ccLS 4-5). Gemini 3.0 showed comparatively strong performance in extracting individual imaging features, such as T2-weighted imaging (T2WI) hyperintensity. However, all models showed reduced performance for features requiring multi-step interpretation, particularly the arterial-to-delayed enhancement ratio (ADR), with Qwen 3.5 showing the weakest performance for these complex features. In the pathology subgroup of the internal cohort (n = 237), expert report-based scores yielded an AUC of 0.799 for identifying ccRCC, with LLM-assigned scores showing AUCs of 0.768-0.791 and small AUC differences from the expert standard (-0.031 to -0.008) with overlapping 95% CIs.
Conclusion
LLMs employing multi-step prompting, particularly Gemini 3.0, demonstrated favorable performance in assigning ccLS v2.0 categories from free-text reports. Despite limitations in complex feature reasoning, these models showed potential for supporting automated ccLS assessment in radiology workflows; however, in their current implementation, they should be considered decision-support tools that require expert verification rather than autonomous, unsupervised scoring.