A multicenter benchmark and clinically structured metric for coronary CTA report generation
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Abstract (arXiv)
Reliable evaluation of automated coronary computed tomography angiography (CCTA) report generation requires standardized multicentre benchmarks and clinically structured metrics. We established a four-centre benchmark comprising 3,021 CCTA series from 818 patient-report pairs to evaluate seven open-source three-dimensional vision-language models. We developed CSMCCTA, a clinically structured metric for CCTA report evaluation, with patient-, vessel-, and segment-level variables defined according to clinical guidelines. Report pairs are compared at the finest shared anatomical level, and the contributions of different clinical components are weighted based on expert assessments. We estimated these weights using 70 expert-scored cases and evaluated clinical alignment in a non-overlapping set of 30 cases. CSMCCTA showed a strong correlation with radiologist scores (Pearson's r=0.97, p<0.001), exceeding the next-best metric, FORTE (r=0.70), by 0.27, and agreed with expert preferences in 115 of 160 pairwise comparisons (71.9%). Under controlled perturbations, CSMCCTA remained stable to clinically equivalent wording and decreased monotonically with progressive information omission. In the multicenter benchmark, the CCTA-trained C2RG model achieved the highest CSMCCTA scores across all four hospitals, although its performance remained far from optimal. In contrast, CCTA-irrelevant reports accounted for up to 98.7% of the outputs from generalist models. Together, the benchmark provides a standardized setting for model comparison, while CSMCCTA enables clinically structured evaluation of finding agreement and anatomical specificity. These results support a more clinically aligned and anatomically resolved approach to evaluating CCTA report generation. Code is available at https://openi.pcl.ac.cn/OpenMedIA/CSMCCTA.
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References · 14
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
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- 2024M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Modelscited by 154
- 2023Interactive and Explainable Region-guided Radiology Report Generationcited by 139
- 2024GREEN: Generative Radiology Report Evaluation and Error Notationcited by 124
- 2023R2GenGPT: Radiology Report Generation with Frozen LLMscited by 111
- 2024Merlin: A Computed Tomography Vision-Language Foundation Model and Datasetcited by 97
- 2025Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understandingcited by 96
- 2024RaTEScore: A Metric for Radiology Report Generationcited by 65
- 2024Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generationcited by 30
- 2023Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generationcited by 23
- 2018A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiographycited by 16
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