A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
For radiologists and patients, this work provides a safer, contrast-free alternative for abdominal imaging, reducing risks and workload, though the performance is moderate and incremental.
This paper introduces a multi-center benchmark for abdominal disease diagnosis and report generation from non-contrast CT, achieving an average multi-organ AUC of 69.1% on internal and 63.1% on external cohorts, demonstrating that NCCT retains diagnostic signals for contrast-enhanced findings.
Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload. To address these challenges, we introduce a novel multi-center benchmark for multi-organ abdominal disease diagnosis and automated radiology report generation, which learns to synthesize contrast-enhanced findings from single-phase non-contrast CT (NCCT). To support this, we curated a large-scale dataset of paired NCCT-CECT studies and their corresponding contrast-enhanced radiology reports from two centers, partitioned into internal sets and an external validation cohort. Under a unified evaluation protocol, we benchmarked five contemporary deep learning architectures encompassing chest-specific, abdomen-specific, and general-purpose multimodal domains. Extensive experiments demonstrate that NCCT retains diagnostic signals, achieving an average multi-organ AUC of 69.1% on the internal cohort and 63.1% on the external cohort, respectively. By releasing this dataset and standardized benchmark publicly, this study aims to catalyze future research into safer, resource-efficient, and globally accessible contrast-free abdominal imaging workflows. Code is available at: https://github.com/xmed-lab/TriALS-Report.