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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.16006 |
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| _version_ | 1866910025503473664 |
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| author | Rivera, Juampablo E. Heras Chen, Dickson T. Ren, Tianyi Low, Daniel K. Abacha, Asma Ben Santamaria-Pang, Alberto Kurt, Mehmet |
| author_facet | Rivera, Juampablo E. Heras Chen, Dickson T. Ren, Tianyi Low, Daniel K. Abacha, Asma Ben Santamaria-Pang, Alberto Kurt, Mehmet |
| contents | 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 features used for report generation are predictive of key clinical outcomes, including survival and IDH mutation status, and reports generated by BTReport are more closely aligned with reference clinical reports than existing baselines for RRG. Finally, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetically generated radiology reports produced with BTReport. Code for this project can be found at https://github.com/KurtLabUW/BTReport. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features Rivera, Juampablo E. Heras Chen, Dickson T. Ren, Tianyi Low, Daniel K. Abacha, Asma Ben Santamaria-Pang, Alberto Kurt, Mehmet Computer Vision and Pattern Recognition 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 features used for report generation are predictive of key clinical outcomes, including survival and IDH mutation status, and reports generated by BTReport are more closely aligned with reference clinical reports than existing baselines for RRG. Finally, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetically generated radiology reports produced with BTReport. Code for this project can be found at https://github.com/KurtLabUW/BTReport. |
| title | BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.16006 |