CTest-Metric: A Unified Framework to Assess Clinical Validity of Metrics for CT Report Generation

• URL: http://arxiv.org/abs/2601.11488v1
• Date: Fri, 16 Jan 2026 18:09:19 GMT
• Title: CTest-Metric: A Unified Framework to Assess Clinical Validity of Metrics for CT Report Generation
• Authors: Vanshali Sharma, Andrea Mia Bejar, Gorkem Durak, Ulas Bagci,
• Abstract summary: We present CTest-Metric, a first unified metric assessment framework with three modules determining the clinical feasibility of metrics for CT RRG.<n>The modules test: (i) Writing Style Generalizability (WSG) via LLM-based rephrasing; (ii) Synthetic Error Injection (SEI) at graded severities; and (iii) Metrics-vs-Expert correlation (MvE) using clinician ratings on 175 "disagreement" cases.<n>Eight widely used metrics (BLEU, ROUGE, METEOR, BERTScore-F1, F1-RadGraph, Ra
• Score: 8.08950963137043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the generative AI era, where even critical medical tasks are increasingly automated, radiology report generation (RRG) continues to rely on suboptimal metrics for quality assessment. Developing domain-specific metrics has therefore been an active area of research, yet it remains challenging due to the lack of a unified, well-defined framework to assess their robustness and applicability in clinical contexts. To address this, we present CTest-Metric, a first unified metric assessment framework with three modules determining the clinical feasibility of metrics for CT RRG. The modules test: (i) Writing Style Generalizability (WSG) via LLM-based rephrasing; (ii) Synthetic Error Injection (SEI) at graded severities; and (iii) Metrics-vs-Expert correlation (MvE) using clinician ratings on 175 "disagreement" cases. Eight widely used metrics (BLEU, ROUGE, METEOR, BERTScore-F1, F1-RadGraph, RaTEScore, GREEN Score, CRG) are studied across seven LLMs built on a CT-CLIP encoder. Using our novel framework, we found that lexical NLG metrics are highly sensitive to stylistic variations; GREEN Score aligns best with expert judgments (Spearman~0.70), while CRG shows negative correlation; and BERTScore-F1 is least sensitive to factual error injection. We will release the framework, code, and allowable portion of the anonymized evaluation data (rephrased/error-injected CT reports), to facilitate reproducible benchmarking and future metric development.

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