ArticleTranslational cancer research2023
Predictive accuracy of machine learning for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients: a systematic review and meta-analysis.
Article in Translational cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 2 citations in OpenAlex.
- MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy.Frontiers in neurology · 2024Article
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8 authors at 5 institutions in 4 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Radiotherapy is a common treatment for nasopharyngeal carcinoma (NPC) but can cause radiation-induced temporal lobe injury (RTLI), resulting in irreversible damage. Predicting RTLI at the early stage may help with that issue by personalized adjustment of radiation dose based on the predicted risk. Machine learning (ML) models have recently been used to predict RTLI but their predictive accuracy remains unclear because the reported concordance index (C-index) varied widely from around 0.31 to 0.97. Therefore, a meta-analysis was needed. Methods: The PubMed, Web of Science, Embase, and Cochrane Library databases were searched from inception to November 2022. Studies that fully develop one or more ML risk models of RTLI after radiotherapy for NPC were included. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess the risk of bias in the included research. The primary outcome of this review was the C-index, specificity (Spe), and sensitivity (Sen). Results: The meta-analysis included 14 studies with 15,573 NPC patients reporting a total of 72 prediction models. Overall, 94.44% of models were found to have a high risk of bias. Radiomics was included in 57 models, dosimetric predictors in 28, and clinical data in 27. The pooled C-index for ML models predicting RTLI was 0.77 [95% confidence interval (CI): 0.75-0.79] in the training set and 0.78 (95% CI: 0.75-0.81) in the validation set. The pooled Sen was 0.75 (95% CI: 0.69-0.80) in the training set and 0.70 (95% CI: 0.66-0.73) in the validation set and the pooled Spe was 0.78 (95% CI: 0.73-0.82) in the training set and 0.79 (95% CI: 0.75-0.82) in the validation set. Models with radiomics and clinical data achieved the most excellent discriminative performance, with a pooled C-index of 0.895. Conclusions: ML models can accurately predict RTLI at an early stage, allowing for timely interventions to prevent further damage. The kind of ML methods and the selection of predictors may influence the predictive accuracy.
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