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, Jacob Ruzevick, Asma Ben Abacha, Alberto Santamaria-Pang, Mehmet Kurt
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:1445-1472, 2026.

Abstract

Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology RRG has been limited due to a scarcity in open paired image-report datasets. Here, we introduce BTReport, an open-source framework for brain tumor RRG that constructs natural language radiology reports using reliably extracted quantitative imaging features. Unlike existing approaches that rely on general-purpose or fine-tuned vision-language models for both image interpretation and report composition, BTReport performs deterministic feature extraction of clinically-relevant features, then uses large language models only for syntactic structuring and narrative synthesis. By separating RRG into deterministic feature extraction and report generation stages, synthetically generated reports are completely interpretable and contain reliable numerical measurements, a key component lacking in existing RRG frameworks. We validate the clinical relevance of BTReport-derived features, and demonstrate that BTReport-generated reports more closely resemble reference clinical reports when compared to existing baseline RRG methods. To further research in neuro-oncology RRG, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetic radiology reports generated with BTReport, and BTReview, a web-based platform for validating the clinical quality of synthetically generated radiology reports.

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-heras-rivera26a, title = {BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features}, author = {Heras Rivera, Juampablo E. and Chen, Dickson T. and Ren, Tianyi and Low, Daniel K. and Ruzevick, Jacob and Ben Abacha, Asma and Santamaria-Pang, Alberto and Kurt, Mehmet}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {1445--1472}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/heras-rivera26a/heras-rivera26a.pdf}, url = {https://proceedings.mlr.press/v315/heras-rivera26a.html}, abstract = {Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology RRG has been limited due to a scarcity in open paired image-report datasets. Here, we introduce BTReport, an open-source framework for brain tumor RRG that constructs natural language radiology reports using reliably extracted quantitative imaging features. Unlike existing approaches that rely on general-purpose or fine-tuned vision-language models for both image interpretation and report composition, BTReport performs deterministic feature extraction of clinically-relevant features, then uses large language models only for syntactic structuring and narrative synthesis. By separating RRG into deterministic feature extraction and report generation stages, synthetically generated reports are completely interpretable and contain reliable numerical measurements, a key component lacking in existing RRG frameworks. We validate the clinical relevance of BTReport-derived features, and demonstrate that BTReport-generated reports more closely resemble reference clinical reports when compared to existing baseline RRG methods. To further research in neuro-oncology RRG, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetic radiology reports generated with BTReport, and BTReview, a web-based platform for validating the clinical quality of synthetically generated radiology reports.} }
Endnote
%0 Conference Paper %T BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features %A Juampablo E. Heras Rivera %A Dickson T. Chen %A Tianyi Ren %A Daniel K. Low %A Jacob Ruzevick %A Asma Ben Abacha %A Alberto Santamaria-Pang %A Mehmet Kurt %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-heras-rivera26a %I PMLR %P 1445--1472 %U https://proceedings.mlr.press/v315/heras-rivera26a.html %V 315 %X Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology RRG has been limited due to a scarcity in open paired image-report datasets. Here, we introduce BTReport, an open-source framework for brain tumor RRG that constructs natural language radiology reports using reliably extracted quantitative imaging features. Unlike existing approaches that rely on general-purpose or fine-tuned vision-language models for both image interpretation and report composition, BTReport performs deterministic feature extraction of clinically-relevant features, then uses large language models only for syntactic structuring and narrative synthesis. By separating RRG into deterministic feature extraction and report generation stages, synthetically generated reports are completely interpretable and contain reliable numerical measurements, a key component lacking in existing RRG frameworks. We validate the clinical relevance of BTReport-derived features, and demonstrate that BTReport-generated reports more closely resemble reference clinical reports when compared to existing baseline RRG methods. To further research in neuro-oncology RRG, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetic radiology reports generated with BTReport, and BTReview, a web-based platform for validating the clinical quality of synthetically generated radiology reports.
APA
Heras Rivera, J.E., Chen, D.T., Ren, T., Low, D.K., Ruzevick, J., Ben Abacha, A., Santamaria-Pang, A. & Kurt, M.. (2026). BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:1445-1472 Available from https://proceedings.mlr.press/v315/heras-rivera26a.html.

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