Defining aggressive prostate cancer: a geospatial perspective

• Published in: BMC cancer Vol. 23; no. 1; p. 754
• Main Authors: Wiese, Daniel, DuBois, Tesla D, Sorice, Kristen A, Fang, Carolyn Y, Ragin, Camille, Daly, Mary B, Reese, Adam C, Henry, Kevin A, Lynch, Shannon M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14.08.2023; BioMed Central; BMC
• Subjects: Aggressive prostate cancer; Analysis; Bayesian analysis; Cancer; Care and treatment; Censuses; Classification; Diagnosis; Geospatial; Geospatial data; Immune system; Mathematical models; Methods; Monte Carlo simulation; Mortality; Oncology, Experimental; Patients; Population; Prostate cancer; Ratios; Sociodemographics; Survival analysis; Testing; Tumor staging; United States; United States > US; Pennsylvania; Survival analysis; Geospatial; Prostate cancer; Aggressive prostate cancer
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-023-11281-8

Spatial analysis can identify communities where men are at risk for aggressive prostate cancer (PCan) and need intervention. However, there are several definitions for aggressive PCan. In this study, we evaluate geospatial patterns of 3 different aggressive PCan definitions in relation to PCan-specific mortality and provide methodologic and practical insights into how each definition may affect intervention targets. Using the Pennsylvania State Cancer Registry data (2005-2015), we used 3 definitions to assign "aggressive" status to patients diagnosed with PCan. Definition one (D1, recently recommended as the primary definition, given high correlation with PCan death) was based on staging criteria T4/N1/M1 or Gleason score ≥ 8. Definition two (D2, most frequently-used definition in geospatial studies) included distant SEER summary stage. Definition three (D3) included Gleason score ≥ 7 only. Using Bayesian spatial models, we identified geographic clusters of elevated odds ratios for aggressive PCan (binomial model) for each definition and compared overlap between those clusters to clusters of elevated hazard ratios for PCan-specific mortality (Cox regression). The number of "aggressive" PCan cases varied by definition, and influenced quantity, location, and extent/size of geographic clusters in binomial models. While spatial patterns overlapped across all three definitions, using D2 in binomial models provided results most akin to PCan-specific mortality clusters as identified through Cox regression. This approach resulted in fewer clusters for targeted intervention and less sensitive to missing data compared to definitions that rely on clinical TNM staging. Using D2, based on distant SEER summary stage, in future research may facilitate consistency and allow for standardized comparison across geospatial studies.