Divergent white matter changes in patients with nasopharyngeal carcinoma post-radiotherapy with different outcomes: a potential biomarker for prediction of radiation necrosis

Lin, Xia; Li, Zhipeng; Chen, Shengli; Yang, Yingjie; He, Hualiang; Lv, Xiaofei; Qiu, Yingwei

doi:10.1007/s00330-022-08907-z

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…Following a review of the full text of the papers, we excluded 10 studies ( Table S2 ), primarily because they did not provide a complete ML model. Finally, our review finally included 14 studies ( 4 , 15 , 16 , 21 - 31 ).…”

Section: Resultsmentioning

confidence: 99%

“…LR is the most often used ML approach for building models due to its ease of use and consistently good performance. However, to determine the best-performing model, comparisons of various ML methods based on the same dataset should be made ( 15 , 23 , 24 ). As indicated in the meta-analysis above, SVM appears to have the highest performance, particularly in the validation set, although further comparison studies are needed to confirm this finding.…”

Section: Discussionmentioning

confidence: 99%

“…ML was used to develop predictive models of NPC prognosis and was proven to have great predictive performance ( 14 ). Nevertheless, the predictive accuracy of ML models of RTLI is still debated since it varies from around 0.31 to 0.97, which may be caused by different variables and ML approaches ( 15 , 16 ). Also, there is a lack of relevant evidence-based information.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Gong,

Guo

et al. 2023

Transl Cancer Res

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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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