SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer

2026-08-29

AI Summary

为解决头颈癌CT图像报告自动生成中的幻觉风险和数据稀缺问题，提出SGRNet，利用空间引导和结构化报告方法提高准确性和安全性。

Abstract

Automated radiological report generation can alleviate clinical workloads and eliminate observer variability. However, standard free-text generation models pose hallucination risks in dense regions and fail under data scarcity. We address these challenges in Head and Neck Cancer (HNC) from contrast-enhanced CT (CECT) imaging. To enforce factual safety, we reformulate report generation as an anatomically grounded, multi-label, structured reporting task, predicting localized tumor involvement across a hierarchical clinical schema. To bridge the visual gap from missing metabolic imaging (e.g., PET), we introduce SGRNet (Spatially Guided Radiology Network), incorporating two low-cost spatial priors: automated organ segmentations and weakly supervised tumor localization maps modeled via 3D Gaussian heatmaps. These priors are dynamically integrated via spatial feature modulation to guide the network toward subtle tumor-induced structural alterations. Evaluated on a multi-centric dataset of 184 paired HNC CECT volumes and reports, on five clinically salient, densely packed anatomical subsites, SGRNet achieves a mean Average Precision (mAP) of 0.60, an 8.8 percentage-point absolute improvement over strong volume-only 3D baselines.

Problem

• Automated Radiological Report Generation
• Head and Neck Cancer
• Contrast-Enhanced CT
• Data Scarcity
• Hallucination Risks

Innovation

• Spatially Guided Radiology Network
• automated organ segmentations
• weakly supervised tumor localization maps
• spatial feature modulation