Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review
PurposeAlthough 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 predicti...
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| Format: | Article |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.853472/full |
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| author | Fariba Tohidinezhad Dario Di Perri Catharina M. L. Zegers Jeanette Dijkstra Monique Anten Andre Dekker Wouter Van Elmpt Daniëlle B. P. Eekers Alberto Traverso |
| author_facet | Fariba Tohidinezhad Dario Di Perri Catharina M. L. Zegers Jeanette Dijkstra Monique Anten Andre Dekker Wouter Van Elmpt Daniëlle B. P. Eekers Alberto Traverso |
| author_sort | Fariba Tohidinezhad |
| collection | DOAJ |
| description | PurposeAlthough 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.MethodsMEDLINE 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.ResultsOf 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.ConclusionExisting models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: developing cognitive assessment instruments taking into account the peculiar traits of the different brain tumors and radiation modalities; 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. |
| first_indexed | 2024-12-22T17:11:54Z |
| format | Article |
| id | doaj.art-992f6152dd5742d6b4577110d758ecfa |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-22T17:11:54Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-992f6152dd5742d6b4577110d758ecfa2022-12-21T18:19:03ZengFrontiers Media S.A.Frontiers in Psychology1664-10782022-03-011310.3389/fpsyg.2022.853472853472Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic ReviewFariba Tohidinezhad0Dario Di Perri1Catharina M. L. Zegers2Jeanette Dijkstra3Monique Anten4Andre Dekker5Wouter Van Elmpt6Daniëlle B. P. Eekers7Alberto Traverso8Department of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, BelgiumDepartment of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Medical Psychology, School for Mental Health and Neurosciences (MHeNS), Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Neurology, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, NetherlandsPurposeAlthough 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.MethodsMEDLINE 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.ResultsOf 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.ConclusionExisting models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: developing cognitive assessment instruments taking into account the peculiar traits of the different brain tumors and radiation modalities; 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2022.853472/fullcranial irradiationcognitive dysfunctionneurotoxicitymachine learningartificial intelligence |
| spellingShingle | Fariba Tohidinezhad Dario Di Perri Catharina M. L. Zegers Jeanette Dijkstra Monique Anten Andre Dekker Wouter Van Elmpt Daniëlle B. P. Eekers Alberto Traverso Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review Frontiers in Psychology cranial irradiation cognitive dysfunction neurotoxicity machine learning artificial intelligence |
| title | Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review |
| title_full | Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review |
| title_fullStr | Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review |
| title_full_unstemmed | Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review |
| title_short | Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review |
| title_sort | prediction models for radiation induced neurocognitive decline in adult patients with primary or secondary brain tumors a systematic review |
| topic | cranial irradiation cognitive dysfunction neurotoxicity machine learning artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.853472/full |
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