Neuroanatomical location of brain metastases from solid tumours based on pathology: An analysis of 511 patients with a comparison to the provided clinical history. - Michael Bonert; Alison Berzins; Housne Begum; Jens Schittenhelm; Jian-Qiang Lu; Rosalyn A Juergens; Anand Swaminath; Jean-Claude Cutz; Asghar H Naqvi (PLoS ONE, 2023)
Brain metastases are a frequent occurrence in neuropathology practices. The literature on their neuroanatomical location is frequently derived from radiological analyses. This work examines brain metastases through the lens of pathology specimens. All brain surgical pathology reports for cases accessioned 2011-2020 were retrieved from a laboratory. Specimens were classified by neuroanatomical location, diagnosis and diagnostic category with a hierarchical free text string-matching algorithm (HFTSMA) and also subsequently audited. All reports classified as probable metastasis were reviewed by a pathologist. The provided history was compared to the final categorization by a pathologist. The cohort had 4,625 cases. The HFTSMA identified 854 cases (including metastases from a definite primary, metastases from primary not known and improperly classified cases). 514/854 cases had one definite primary site per algorithm and on report review 538/854 cases were confirmed as such. The 538 cases originated from 511 patients. Primaries from breast, gynecologic tract, and gastrointestinal tract not otherwise specified were most frequently found in the cerebellum. Kidney metastases were most frequently found in the occipital lobe. Lung, metastatic melanoma and colorectal primaries were most commonly found in the frontal lobe. The provided clinical history predicted the primary in 206 cases (40.3%), was discordant in 17 cases (3.3%) and non-contributory in 280 cases (54.8%). The observed distribution of the metastatic tumours in the brain is dependent on the primary site. In the majority (54.8%) of cases, the provided clinical history was non-contributory; this suggests surgeon-pathologist communication may have the potential for optimization.