BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features
Juampablo E. Heras Rivera, Dickson T. Chen, Tianyi Ren, Daniel K. Low, Asma Ben Abacha, Alberto Santamaria-Pang, Mehmet Kurt

TL;DR
BTReport introduces an interpretable brain tumor radiology report generation framework using deterministic imaging features, improving clinical relevance and report accuracy while providing a new dataset for neuro-oncology research.
Contribution
It presents a novel deterministic feature extraction approach for brain tumor report generation and introduces a new dataset, BTReport-BraTS, for neuro-oncology applications.
Findings
Generated reports are more aligned with clinical references.
Extracted features predict key clinical outcomes.
Reports are less prone to hallucinations.
Abstract
Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology has been limited due to a lack of open paired image-report datasets. Here, we introduce BTReport, an open-source framework for brain tumor RRG that constructs natural language radiology reports using deterministically extracted imaging features. Unlike existing approaches that rely on large general-purpose or fine-tuned vision-language models for both image interpretation and report composition, BTReport performs deterministic feature extraction for image analysis and uses large language models only for syntactic structuring and narrative formatting. By separating RRG into a deterministic feature extraction step and a report generation step, the generated reports are completely interpretable and less prone to hallucinations. We show that the…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Artificial Intelligence in Healthcare and Education · Radiomics and Machine Learning in Medical Imaging
