多中心非增强CT腹部疾病诊断与报告生成基准
A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
这篇论文发布了首个多中心非增强CT诊断基准，用NCCT就能达到CECT六到七成AUC，有望减少造影剂风险和辐射。
该论文提出一个多中心基准，用于从单期非增强CT（NCCT）合成对比增强CT（CECT）特征，实现多器官腹部疾病诊断和自动报告生成。数据集包含来自两个中心的配对NCCT-CECT影像及增强报告，分为内部集和外部验证集。在统一评估协议下，五种深度学习架构（胸部专用、腹部专用、通用多模态）被基准测试。实验表明NCCT保留诊断信号，内部集平均多器官AUC达69.1%，外部集为63.1%。研究公开了数据集和代码（GitHub），旨在推动更安全、低资源、可及的免对比成像工作流。
A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
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.
