Narrative review demonstrates improved detection of cognitive effects in cancer patients, suggesting advanced digital assessments and statistical methods may enhance research sensitivity.
Cancer-Related Cognitive Impairment (CRCI) reflects changes in the cognitive performance of individuals diagnosed and treated for cancer. CRCI occurs in a significant percentage of cancer patients and cancer survivors, in both CNS and non-CNS cancer. However, research findings are mixed with regards to the prevalence, the domains affected, and the size of effects for the non-CNS cancer population. This may partly be due to cognitive effects being relatively subtle in this patient population. To improve the sensitivity of studies to detect CRCI, steps are being taken to establish larger samples, but these remain costly. In this narrative review, we describe methodological and statistical developments that may help to improve the sensitivity of our studies in detecting CRCI. We discuss several forms of supervised and unsupervised digital testing based on traditional tests commonly used in neuropsychological assessment, as well as newer paradigms such as Ecological Momentary Assessment and cognitive neuroscience-based tests. With digital tests, new types of behavioral data become available, which allows for more sophisticated data analyses. Additionally, we discuss several statistical approaches to the analysis of cognitive data that can be used to improve sensitivity, such as Item Response Theory, Bayesian mixed-effects models and Machine Learning (see Table 1). We close with several recommendations for the field, regarding the large number of digital test batteries that are becoming available, uncontrolled false positive rates, and inconsistent use of cognitive domains.[Table: see text].
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Rentergem et al. (2025) studied this question.
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