Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China.
School of Mathematics, Sun Yat-Sen University, Guangzhou 510060, PR China.
EBioMedicine. 2019 Apr;42:270-280. doi: 10.1016/j.ebiom.2019.03.050. Epub 2019 Mar 27.
To identify a radiomics signature to predict local recurrence in patients with non-metastatic T4 nasopharyngeal carcinoma (NPC).
A total of 737 patients from Sun Yat-sen University Cancer Center (training cohort: n = 360; internal validation cohort: n = 120) and Wuzhou Red Cross Hospital (external validation cohort: n = 257) underwent feature extraction from the largest axial area of the tumor on pretreatment magnetic resonance imaging scans. Feature selection was based on the prognostic performance and feature stability in the training cohort. Radscores were generated using the Cox proportional hazards regression model with the selected features in the training cohort and then validated in the internal and external validation cohorts. We also constructed a nomogram for predicting local recurrence-free survival (LRFS).
Eleven features were selected to construct the Radscore, which was significantly associated with LRFS. For the training, internal validation, and external validation cohorts, the Radscore (C-index: 0.741 vs. 0.753 vs. 0.730) outperformed clinical prognostic variables (C-index for primary gross tumor volume: 0.665 vs. 0.672 vs. 0.577; C-index for age: 0.571 vs. 0.629 vs. 0.605) in predicting LRFS. The generated radiomics nomogram, which integrated the Radscore and clinical variables, exhibited a satisfactory prediction performance (C-index: 0.810 vs. 0.807 vs. 0.753). The nomogram-defined high-risk group had a shorter LRFS than did the low-risk group (5-year LRFS: 73.6% vs. 95.3%, P < .001; 79.6% vs 95.8%, P = .006; 85.7% vs 96.7%, P = .005).
The Radscore can reliably predict LRFS in patients with non-metastatic T4 NPC, which might guide individual treatment decisions. FUND: This study was funded by the Health & Medical Collaborative Innovation Project of Guangzhou City, China.
为了识别预测非转移性 T4 期鼻咽癌（NPC）局部复发的放射组学特征。
中山大学肿瘤防治中心（训练队列：n=360；内部验证队列：n=120）和梧州市红十字会医院（外部验证队列：n=257）的 737 名患者在治疗前磁共振成像扫描的肿瘤最大轴位进行特征提取。基于训练队列的预后表现和特征稳定性进行特征选择。在训练队列中使用 Cox 比例风险回归模型生成 Radscore，并在内部和外部验证队列中进行验证。我们还构建了一个预测局部无复发生存率（LRFS）的列线图。
选择 11 个特征来构建 Radscore，该特征与 LRFS 显著相关。对于训练、内部验证和外部验证队列，Radscore（C 指数：0.741 vs. 0.753 vs. 0.730）优于临床预后变量（原发性大体肿瘤体积的 C 指数：0.665 vs. 0.672 vs. 0.577；年龄的 C 指数：0.571 vs. 0.629 vs. 0.605），可用于预测 LRFS。整合了 Radscore 和临床变量的生成放射组学列线图表现出令人满意的预测性能（C 指数：0.810 vs. 0.807 vs. 0.753）。列线图定义的高危组的 LRFS 短于低危组（5 年 LRFS：73.6% vs. 95.3%，P<0.001；79.6% vs 95.8%，P=0.006；85.7% vs 96.7%，P=0.005）。
Radscore 可可靠地预测非转移性 T4 NPC 患者的 LRFS，这可能有助于指导个体化治疗决策。
本研究由广州市健康医疗协同创新重大专项资助。
