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.
ABSTRACT
Absence of the falx cerebri is a rare radiological finding, which is generally described in relation with varying degrees of holoprosencephaly or other structural central nervous system anomalies. We present a case of non-syndromic, asymptomatic absence of falx cerebri in an elderly patient, which was discovered incidentally on a computed tomography examination of the head. The superior sagittal sinus was narrowed, but present. Relevant anatomic and embryological considerations are discussed, as well as potential implications for clinical practice.
