Deep-Transfer-Learning–Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer
Deep-transfer-learning-based NLP model of serial CT reports can predict the survival of patients with pancreatic cancer. Clinical decisions can be supported by the developed model, with survival information extracted solely from serial radiology reports.
Authors
- Sunkyu Kim (ORCID: https://orcid.org/0000-0002-0240-6210)
- Mi‐Suk Park (ORCID: https://orcid.org/0000-0001-5817-2444)
- Choong‐kun Lee (ORCID: https://orcid.org/0000-0001-5151-5096)
- Chang Moo Kang (ORCID: https://orcid.org/0000-0002-5382-4658)
- Ho Kyoung Hwang (ORCID: https://orcid.org/0000-0003-4064-7776)
- Sang Joon Shin (ORCID: https://orcid.org/0000-0001-5350-7241)
- Michael Cecchini (ORCID: https://orcid.org/0000-0003-3504-0128)
- Seung‐seob Kim (ORCID: https://orcid.org/0000-0001-6071-306X)
- Hye Jin Choi (ORCID: https://orcid.org/0000-0001-5917-1400)
- Jaewoo Kang (ORCID: https://orcid.org/0000-0001-6798-9106)
- Jonggyu Choi (ORCID: https://orcid.org/0009-0004-0672-5261)
- Eejung Kim
- Sung Hyun Kim (ORCID: https://orcid.org/0000-0001-7683-9687)
Institutions
- Yonsei University (KR)
- Korea University (KR)
- Severance Hospital (KR)
- Yale University (US)
- Dana-Farber Cancer Institute (US)
- Syneos Health (South Korea) (KR)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1200/cci.24.00021
- Citations
- 3
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.85