Personalized Impression Generation for PET Reports Using Large Language Models

Author:

Tie Xin,Shin Muheon,Pirasteh Ali,Ibrahim Nevein,Huemann Zachary,Castellino Sharon M.,Kelly Kara M.,Garrett John,Hu Junjie,Cho Steve Y.,Bradshaw Tyler J.ORCID

Funder

Imaging and Radiology Oncology Core Rhode Island

Biomarker, Imaging and Quality of Life Studies Funding Program

NCTN Operations Center Grant

NCTN Statistics and Data Center Grant

St. Baldrick’s Foundation

National Institute of Biomedical Imaging and Bioengineering

Publisher

Springer Science and Business Media LLC

Reference49 articles.

1. R. D. Niederkohr et al., “Reporting Guidance for Oncologic 18 F-FDG PET/CT Imaging,” J Nucl Med, vol. 54, no. 5, pp. 756–761, May 2013. https://doi.org/10.2967/jnumed.112.112177.

2. M. P. Hartung, I. C. Bickle, F. Gaillard, and J. P. Kanne, “How to Create a Great Radiology Report,” RadioGraphics, vol. 40, no. 6, pp. 1658–1670, Oct. 2020. https://doi.org/10.1148/rg.2020200020.

3. Y. Zhang, D. Y. Ding, T. Qian, C. D. Manning, and C. P. Langlotz, “Learning to Summarize Radiology Findings,” in Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, Brussels, Belgium: Association for Computational Linguistics, 2018, pp. 204–213. https://doi.org/10.18653/v1/W18-5623.

4. J. Hu, Z. Li, Z. Chen, Z. Li, X. Wan, and T.-H. Chang, “Graph Enhanced Contrastive Learning for Radiology Findings Summarization.” arXiv, Jun. 08, 2022. Accessed: Mar. 02, 2023. [Online]. Available: http://arxiv.org/abs/2204.00203

5. J.-B. Delbrouck, M. Varma, and C. P. Langlotz, “Toward expanding the scope of radiology report summarization to multiple anatomies and modalities.” arXiv, Nov. 18, 2022. Accessed: Mar. 02, 2023. [Online]. Available: http://arxiv.org/abs/2211.08584

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