There is consensus that benzene exposure is causally related to acute myeloid leukemia (AML), but accurate description of the AML-benzene exposure response curve (ERC) is still needed for risk assessment. Scholten and colleagues hypothesized that using data from both human and animal studies could increase precision of the estimated ERC for benzene induced AML. The authors found that risk estimates based on the complete dataset (23 experimental and observation studies) were more precise, but also that harmonization steps required to fit the Bayesian meta-regression model involve a range of assumptions that need to be critically evaluated, as they seem crucial for successful implementation.

Screening reduces lung cancer mortality, but the low specificity of current eligibility criteria has blocked implementation. Risk biomarkers (e.g. AHRR methylation of leukocyte DNA) might mitigate this. Jacobsen and colleagues utilized the Copenhagen City Heart Study which included >9,200 individuals with detailed smoking information and >20 years...

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