Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients with Primary or Secondary Brain Tumors: A Systematic Review

Abstract

Purpose: Although an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.Methods: MEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy”, “brain”, “prediction model”, and “neurocognitive impairments”. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.Results: Of 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n=7) and the trail making tests (n=4) were the most frequent outcome assessment tools. All studies used regression (n=14 linear, n=8 logistic, and n=4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.Conclusion: Existing models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: develop a standardized instrument for neurocognitive assessment in patients with brain tumors; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

Chronic Diseases: A Comprehensive Review of Applications for Prediction of Obesity

“…Current approaches typically employ one-size-fits-all strategies that do not account for the complex interplay of genetic, environmental, and personal factors that influence obesity [36,37]. Machine Learning (ML) and other advanced predictive technologies offer promising tools to fill this gap [38,39]. These technologies can analyse vast arrays of data, from genetic profiles to lifestyle habits, providing personalized insights that can guide more effective intervention strategies [39,40].…”

Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review

“… 6 Machine learning used for decision support was defined as algorithms used to provide some form of input into human decision-making. 8 Machine learning tools used for automation without any input from the clinician were excluded. For example, machine learning tools used to predict patients at higher risk of a particular outcome without any decisions or interventions required by the clinician were excluded.…”

“…Only decision support tools using machine learning for clinical tasks defined as ‘tasks generally performed by qualified healthcare providers related to the assessment, intervention and evaluation of health-related issues and procedures’ or epidemiological tasks specified as ‘tasks related to more accurately identifying the health needs and outcomes of people within a given population’ were included. 8 Machine learning used for operational tasks defined as ‘tasks related to activities that are ancillary to clinical tasks but necessary or valuable in the delivery of services (generally more administrative)’ was excluded. 8 Studies that did not report implementation strategies for the machine learning interventions that were used for patient care were excluded.…”

“… 8 Machine learning used for operational tasks defined as ‘tasks related to activities that are ancillary to clinical tasks but necessary or valuable in the delivery of services (generally more administrative)’ was excluded. 8 Studies that did not report implementation strategies for the machine learning interventions that were used for patient care were excluded.…”

“…Several systematic reviews have examined the use of machine learning for clinical decision-making focused on specific tasks, such as stroke and risk stratification, 1 preterm birth prediction, 2 and predicting radiation-induced neurocognitive decline. 8 However, much less focus has been on how to implement machine learning methods in hospital settings. For example, a recent scoping review found very few clinical decision support systems that used machine learning were implemented in the hospital setting.…”