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. 2023 Apr 12:13:1160167.
doi: 10.3389/fonc.2023.1160167. eCollection 2023.

A scoping review of natural language processing of radiology reports in breast cancer

Ashirbani Saha  1   2 , Levi Burns  3 , Ameya Madhav Kulkarni  4
Affiliations

A scoping review of natural language processing of radiology reports in breast cancer

Ashirbani Saha et al. Front Oncol. .

Abstract

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.

Keywords: artificial intelligence; breast cancer; deep learning; machine learning; mammography; natural language processing; radiology report.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Potential NLP tasks at various levels of electronic healthcare data and some corresponding applications of NLP in oncology. The stages of the cancer continuum of care are as indicated by Cancer Care Ontario (7). The example applications listed may overlap or have mutual influence on each other.
Figure 2
Figure 2
PRISMA diagram demonstrating the search and identification process for the scoping review.
Figure 3
Figure 3
Synthetic analysis (Sankey plot) showing the relationship among publication year, venue, diseases studied, data type, number of institutions in the dataset, radiology report language, and country.
Figure 4
Figure 4
Synthetic analysis (Sankey plot) showing the relationship among NLP development and/or evaluation (Dev/Eval), year of publication, NLP approach, BERT usage, counts of breast cancer patients and radiology reports, and evaluation process. (DL, deep learning; ML, machine learning; RNN, recurrent neural network; BERT, bidirectional encoder representations from transformers).
See this image and copyright information in PMC

References

    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. . Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J Clin (2021) 71(3):209–49. doi: 10.3322/caac.21660 - DOI - PubMed
    1. Autier P, Boniol M, LaVecchia C, Vatten L, Gavin A, Héry C, et al. . Disparities in breast cancer mortality trends between 30 European countries: retrospective trend analysis of WHO mortality database. BMJ (2010) 341(7768):335. doi: 10.1136/bmj.c3620 - DOI - PMC - PubMed
    1. Lei S, Zheng R, Zhang S, Wang S, Chen R, Sun K, et al. . Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020. Cancer Commun (2021) 41(11):1183–94. doi: 10.1002/cac2.12207 - DOI - PMC - PubMed
    1. Marmot MG, Altman DG, Cameron DA, Dewar JA, Thompson SG, Wilcox M. The benefits and harms of breast cancer screening: an independent review. Br J Cancer (2013) 108(11):2205–40. doi: 10.1038/bjc.2013.177 - DOI - PMC - PubMed
    1. Duggan C, Trapani D, Ilbawi AM, Fidarova E, Laversanne M, Curigliano G, et al. . National health system characteristics, breast cancer stage at diagnosis, and breast cancer mortality: a population-based analysis. Lancet Oncol (2021) 22(11):1632–42. doi: 10.1016/S1470-2045(21)00462-9 - DOI - PubMed

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