  Objective: There is a lot of information about cancer in Electronic Health
Record (EHR) notes that can be useful for biomedical research provided natural
language processing (NLP) methods are available to extract and structure this
information. In this paper, we present a scoping review of existing clinical
NLP literature for cancer. Methods: We identified studies describing an NLP
method to extract specific cancer-related information from EHR sources from
PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion
criteria were used in this study. We excluded articles where the extraction
techniques used were too broad to be represented as frames and also where very
low-level extraction methods were used. 79 articles were included in the final
review. We organized this information according to frame semantic principles to
help identify common areas of overlap and potential gaps. Results: Frames were
created from the reviewed articles pertaining to cancer information such as
cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis,
prostate cancer diagnosis and pain in prostate cancer patients. These frames
included both a definition as well as specific frame elements (i.e. extractable
attributes). We found that cancer diagnosis was the most common frame among the
reviewed papers (36 out of 79), with recent work focusing on extracting
information related to treatment and breast cancer diagnosis. Conclusion: The
list of common frames described in this paper identifies important
cancer-related information extracted by existing NLP techniques and serves as a
useful resource for future researchers requiring cancer information extracted
from EHR notes. We also argue, due to the heavy duplication of cancer NLP
systems, that a general purpose resource of annotated cancer frames and
corresponding NLP tools would be valuable.
