基于 MRI 的随机生存森林模型提高了局部晚期鼻咽癌诱导化疗加同期放化疗无进展生存的预测能力。
MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma.
机构信息
Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, No. 71, Hedi Rd, Nanning, 530021, Guangxi, China.
Southern Medical University, Guangzhou, China.
出版信息
BMC Cancer. 2022 Jul 6;22(1):739. doi: 10.1186/s12885-022-09832-6.
BACKGROUND
The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT).
METHODS
Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients.
RESULTS
A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05).
CONCLUSION
The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients.
背景
本研究旨在探讨随机生存森林（RSF）模型和 Cox 模型在预测诱导化疗联合同期放化疗（IC+CCRT）后局部区域晚期鼻咽癌（LANPC）患者无进展生存期（PFS）中的应用价值。
方法
对接受治疗前磁共振成像（MRI）扫描的合格 LANPC 患者进行放射组学特征提取。对训练队列中的患者的放射组学和临床特征进行 RSF 分析，以预测 PFS，并在测试队列中进行测试。使用受试者工作特征（ROC）曲线下面积（AUC）和 Delong 检验评估具有临床和影像学预测因子的 RSF 模型的性能，并与基于临床和影像学参数的 Cox 模型进行比较。此外，使用 Kaplan-Meier 方法对患者进行风险分层。
结果
共纳入 294 例 LANPC 患者（训练队列 206 例；测试队列 88 例），并在治疗前进行了 MRI 扫描。临床 Cox 模型、放射组学 Cox 模型、临床+放射组学 Cox 模型和临床+放射组学 RSF 模型预测 LANPC 患者 3 年和 5 年 PFS 的 AUC 值分别为[0.545 与 0.648 与 0.648 与 0.899（训练队列）和 0.566 与 0.736 与 0.730 与 0.861（测试队列）；0.556 与 0.604 与 0.611 与 0.897（训练队列）和 0.591 与 0.661 与 0.676 与 0.847（测试队列）]。Delong 检验显示，RSF 模型与其他三个 Cox 模型均具有统计学意义，且 RSF 模型显著提高了预测性能（P<0.001）。此外，在 RSF 模型中，高危组的 PFS 低于低危组（P<0.001），而 Cox 模型中则无差异（P>0.05）。
结论
RSF 模型可能是预测 LANPC 患者预后和风险分层的一种有潜力的工具。
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