Evidence map›Paper›PMID 41163010›Full record

ArticleRadiation oncology (London, England)2025

Composite quantitative structural magnetic resonance imaging-based risk scoring model for predicting radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma: a novel risk stratification model.

Jie Pan, Jiahui Liang, Yunpeng Li, Shishi Chen, Aner Deng, Lizhi Liu, Gui Fu, Xiaofei Lv

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiation oncology (London, England), 2025. 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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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jie Pan *Department 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.
Jiahui Liang *Department 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.
Yunpeng LiDepartment 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.
Shishi ChenDepartment 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.
Aner DengDepartment 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.
Lizhi LiuDepartment 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. liulizh@sysucc.org.cn.
Gui FuDepartment 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. fugui@sysucc.org.cn.
Xiaofei LvDepartment 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. lvxf@sysucc.org.cn.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2024A1515013278
6 · The paper itself

Abstract

backgroundRadiation-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.

methodsLongitudinal 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.

resultsCombining 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).

conclusionsOur 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.

Indexed as

Magnetic Resonance ImagingNasopharyngeal CarcinomaNasopharyngeal NeoplasmsRadiation InjuriesTemporal LobeAdultAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedNecrosisPrognosisRetrospective StudiesRisk AssessmentEarly predictionNasopharyngeal carcinomaQuantitative MRI indicatorsRisk stratificationTemporal lobe necrosis

Identifiers

PMID41163010
PMCPMC12574204

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.