A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT

2026-06-15

AI Summary

This study addresses the risks of contrast-induced nephropathy, patient burden, and radiologist workload associated with multiphase contrast-enhanced CT in abdominal disease diagnosis by introducing the first multicenter, publicly available benchmark that synthesizes diagnostic information from single-phase non-contrast CT. The framework integrates five state-of-the-art deep learning architectures—including chest-specific, abdomen-specific, and general multimodal models—trained and evaluated on paired non-contrast/contrast CT scans and corresponding radiology reports to enable multi-organ disease diagnosis and automated report generation. On internal and external validation sets, the models achieved average multi-organ AUCs of 69.1% and 63.1%, respectively, demonstrating that non-contrast CT contains diagnostically meaningful signals. This work lays the foundation for a contrast-free, resource-efficient, and globally scalable imaging diagnostic pipeline.

Abstract

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.

Problem

• non-contrast CT
• abdominal disease diagnosis
• radiology report generation
• contrast-free imaging
• multi-center benchmark

Innovation

• non-contrast CT
• contrast synthesis
• automated report generation
• multi-center benchmark
• abdominal disease diagnosis