Predictive accuracy of machine learning for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients: a systematic review and meta-analysis

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.

MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy

“…Radiomics turns the deep-seated feature information hidden in conventional medical images into quantitative data invisible to naked eyes (8,9). At present, there have been several studies that use MRI at different time points to construct radiomics models for predicting RTLI in NPC (10)(11)(12)(13)(14)(15)(16). Some studies have developed radiomics nomogram models based on MRI at the end of intensity modulated radiotherapy (IMRT) to predict the RTLI in NPC patients, and these models have shown outstanding predictive performance (11,13,15).…”