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

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.

Large language model-based information extraction from free-text radiology reports: a scoping review protocol

“… 15 While the former is not freely available, the latter searches only Google Scholar and includes only one study based on LLMs. More recent reviews include a specific scoping review on the application of NLP to reports, specifically related to breast cancer 16 and a systematic review on the application of deep learning-based NLP methods in radiology, although this only includes sources of evidence (SOE) up to 2019. 17 Recent studies have been conducted on deep learning methods, including those based on architectures such as bidirectional long short-term memory, recurrent neural network and gated recurrent unitarchitectures.…”