A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation

• URL: http://arxiv.org/abs/2506.23584v2
• Date: Thu, 16 Oct 2025 06:21:00 GMT
• Title: A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
• Authors: Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun, Bruce Daniel Steinberg, Russell Terry, Jie Xu,
• Abstract summary: We propose a framework for automatic renal CT report generation.<n>In Stage 1, a multi-task learning model detects structured clinical features from each 2D image.<n>In Stage 2, a vision-language model generates free-text reports conditioned on the image and the detected features.
• Score: 4.408787333571913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective Renal cancer is a common malignancy and a major cause of cancer-related deaths. Computed tomography (CT) is central to early detection, staging, and treatment planning. However, the growing CT workload increases radiologists' burden and risks incomplete documentation. Automatically generating accurate reports remains challenging because it requires integrating visual interpretation with clinical reasoning. Advances in artificial intelligence (AI), especially large language and vision-language models, offer potential to reduce workload and enhance diagnostic quality. Methods We propose a clinically informed, two-stage framework for automatic renal CT report generation. In Stage 1, a multi-task learning model detects structured clinical features from each 2D image. In Stage 2, a vision-language model generates free-text reports conditioned on the image and the detected features. To evaluate clinical fidelity, generated clinical features are extracted from the reports and compared with expert-annotated ground truth. Results Experiments on an expert-labeled dataset show that incorporating detected features improves both report quality and clinical accuracy. The model achieved an average AUC of 0.75 for key imaging features and a METEOR score of 0.33, demonstrating higher clinical consistency and fewer template-driven errors. Conclusion Linking structured feature detection with conditioned report generation provides a clinically grounded approach to integrate structured prediction and narrative drafting for renal CT reporting. This method enhances interpretability and clinical faithfulness, underscoring the value of domain-relevant evaluation metrics for medical AI development.

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