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

Yiling Li; Fengyuan Gong; Yangyang Guo; Wai Tong Ng; Michael Benedict A Mejia; Wen-Long Nei; Cuicui Wang; Zhanguo Jin

doi:10.21037/tcr-23-859

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

Transl Cancer Res. 2023 Sep 30;12(9):2361-2370. doi: 10.21037/tcr-23-859. Epub 2023 Aug 25.

Authors

Yiling Li¹, Fengyuan Gong², Yangyang Guo¹, Wai Tong Ng^{3

4}, Michael Benedict A Mejia⁵, Wen-Long Nei⁶, Cuicui Wang¹, Zhanguo Jin¹

Affiliations

¹ Vertigo Clinic/Research Center of Aerospace Medicine, Air Force Medical Center, PLA, Beijing, China.
² Graduate School, Hebei North University, Zhangjiakou, China.
³ Clinical Oncology Center and Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
⁴ Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
⁵ Benavides Cancer Institute, UST Hospital, Manila, Philippines.
⁶ Division of Radiation Oncology, National Cancer Center Singapore, Singapore, Singapore.

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.

Keywords: Nasopharyngeal carcinoma (NPC); machine learning (ML); meta-analysis; predictive model; temporal lobe injury.