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A scoping review of natural language processing of radiology reports in breast cancer
Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data regis...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130381/ https://www.ncbi.nlm.nih.gov/pubmed/37124523 http://dx.doi.org/10.3389/fonc.2023.1160167 |
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author | Saha, Ashirbani Burns, Levi Kulkarni, Ameya Madhav |
author_facet | Saha, Ashirbani Burns, Levi Kulkarni, Ameya Madhav |
author_sort | Saha, Ashirbani |
collection | PubMed |
description | Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing. |
format | Online Article Text |
id | pubmed-10130381 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101303812023-04-27 A scoping review of natural language processing of radiology reports in breast cancer Saha, Ashirbani Burns, Levi Kulkarni, Ameya Madhav Front Oncol Oncology Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing. Frontiers Media S.A. 2023-04-12 /pmc/articles/PMC10130381/ /pubmed/37124523 http://dx.doi.org/10.3389/fonc.2023.1160167 Text en Copyright © 2023 Saha, Burns and Kulkarni https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Saha, Ashirbani Burns, Levi Kulkarni, Ameya Madhav A scoping review of natural language processing of radiology reports in breast cancer |
title | A scoping review of natural language processing of radiology reports in breast cancer |
title_full | A scoping review of natural language processing of radiology reports in breast cancer |
title_fullStr | A scoping review of natural language processing of radiology reports in breast cancer |
title_full_unstemmed | A scoping review of natural language processing of radiology reports in breast cancer |
title_short | A scoping review of natural language processing of radiology reports in breast cancer |
title_sort | scoping review of natural language processing of radiology reports in breast cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130381/ https://www.ncbi.nlm.nih.gov/pubmed/37124523 http://dx.doi.org/10.3389/fonc.2023.1160167 |
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