Composite quantitative structural magnetic resonance imaging-based risk scoring model for predicting
Jie Pan1, Jiahui Liang1, Yunpeng Li1
1Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Background:
Radiation-induced temporal lobe necrosis (TLN) impairs long-term survival of patients with nasopharyngeal carcinoma (NPC) after radiotherapy (RT). We aimed to develop an early scoring model that integrats quantitative MRI indicators and clinical factors to enhance TLN risk stratification.
Methods:
Longitudinal MRI scans acquired pre-RT and within 6 months post-RT in 439 patients with NPC (67 necrotic vs. 811 normal temporal lobes) included three-dimensional T1-weighted imaging for gray matter macrostructures and diffusion tensor imaging for white matter microstructures. Clinical and combined models were built using Cox regression, and their performances were compared to evaluate the incremental value of quantitative MRI biomarkers. A composite structural MRI-based risk score (CSS) was constructed for the TLN risk stratification. The incidence of TLN was predicted using a logistic dose-response model.
Results:
Combining quantitative MRI biomarkers with clinical factors, such as age, diabetes, and TL radiation dose, significantly improved predictive accuracy and increased the C-index to 0.888 (P = 0.018). CSS effectively identified individuals at high risk for TLN; those with high CSS had a significantly higher TLN risk than those with low CSS (hazard ratio (HR) [95% confidence interval (CI)] = 3.07 [1.77-5.33], P < 0.001). Individuals with high CSS required a lower 50% tolerance dose for 5-year TLN (72.0 Gy) than those with low CSS (75.2 Gy).
Conclusions:
Our CSS quantitatively characterized the longitudinal structural alterations in the temporal lobes pre- and post-RT. Integrating CSS with clinical and dosimetric parameters enables accurate TLN risk stratification and informs personalized management for patients with NPC.
Clinical Trial Number:
Not applicable.
