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

TL;DR

This paper presents a two-stage AI framework that detects clinical features from renal CT images and then generates accurate, clinically faithful reports, aiming to reduce radiologists' workload and improve diagnostic quality.

Contribution

It introduces a novel, clinically grounded two-stage approach combining feature detection and report generation for renal CTs, enhancing interpretability and accuracy.

Findings

Improved report quality and clinical accuracy with feature detection

Achieved an average AUC of 0.75 for key features

METEOR score of 0.33 indicating better report quality

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.