Abstract：
BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (PFS) in patients with non-metastatic NPC.
METHODS: A cohort of 653 patients with non-metastatic NPC was divided into a training (n = 457) and validation (n = 196) dataset (7:3 ratio). The study included clinicopathological characteristics, hematologic markers, and MRI findings in three machine learning models-random forest (RF), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO)-to predict progression-free survival (PFS). A Venn diagram identified the overlapping signatures from the three ML algorithms. Cox proportional hazard analysis determined the MLDPS for PFS.
RESULTS: The RF, XGBoost, and LASSO algorithms identified six consensus factors from the 33 signatures. Cox proportional hazards analysis showed that the MLDPS includes age, lymphocyte count, number of positive lymph nodes, and regional lymph node density. Additionally, MLDPS effectively stratified prognosis, with low-risk individuals showing better PFS than high-risk individuals (p < 0.001).
CONCLUSIONS: MLDPS, based on clinicopathological characteristics, hematologic markers, and MRI findings, is crucial for guiding clinical management and personalizing treatments for patients with non-metastatic NPC.
METHODS: A cohort of 653 patients with non-metastatic NPC was divided into a training (n = 457) and validation (n = 196) dataset (7:3 ratio). The study included clinicopathological characteristics, hematologic markers, and MRI findings in three machine learning models-random forest (RF), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO)-to predict progression-free survival (PFS). A Venn diagram identified the overlapping signatures from the three ML algorithms. Cox proportional hazard analysis determined the MLDPS for PFS.
RESULTS: The RF, XGBoost, and LASSO algorithms identified six consensus factors from the 33 signatures. Cox proportional hazards analysis showed that the MLDPS includes age, lymphocyte count, number of positive lymph nodes, and regional lymph node density. Additionally, MLDPS effectively stratified prognosis, with low-risk individuals showing better PFS than high-risk individuals (p < 0.001).
CONCLUSIONS: MLDPS, based on clinicopathological characteristics, hematologic markers, and MRI findings, is crucial for guiding clinical management and personalizing treatments for patients with non-metastatic NPC.
摘要：
背景：早期发现高危鼻咽癌（NPC）复发至关重要。我们通过结合三个机器学习（ML）模型来预测非转移性NPC患者的无进展生存期（PFS），从而创建了机器学习衍生的预后特征（MLDPS）。
方法：将653例非转移性NPC患者的队列分为训练（n=457）和验证（n=196）数据集（7：3比例）。该研究包括临床病理特征，血液学标志物,和MRI在三种机器学习模型-随机森林(RF)中的发现，极端梯度提升(XGBoost)，和最小绝对收缩和选择算子（LASSO）-预测无进展生存期（PFS）。维恩图从三种ML算法中识别出重叠的签名。Cox比例风险分析确定了PFS的MLDPS。
结果：RF，XGBoost,LASSO算法从33个签名中确定了6个共识因子。Cox比例风险分析表明，MLDPS包括年龄，淋巴细胞计数,阳性淋巴结数,和区域淋巴结密度。此外,MLDPS有效分层预后，低风险个体比高风险个体表现出更好的PFS（p<0.001）。
结论：MLDPS，根据临床病理特征，血液学标志物,和MRI检查结果,对于指导非转移性NPC患者的临床管理和个性化治疗至关重要。
方法：将653例非转移性NPC患者的队列分为训练（n=457）和验证（n=196）数据集（7：3比例）。该研究包括临床病理特征，血液学标志物,和MRI在三种机器学习模型-随机森林(RF)中的发现，极端梯度提升(XGBoost)，和最小绝对收缩和选择算子（LASSO）-预测无进展生存期（PFS）。维恩图从三种ML算法中识别出重叠的签名。Cox比例风险分析确定了PFS的MLDPS。
结果：RF，XGBoost,LASSO算法从33个签名中确定了6个共识因子。Cox比例风险分析表明，MLDPS包括年龄，淋巴细胞计数,阳性淋巴结数,和区域淋巴结密度。此外,MLDPS有效分层预后，低风险个体比高风险个体表现出更好的PFS（p<0.001）。
结论：MLDPS，根据临床病理特征，血液学标志物,和MRI检查结果,对于指导非转移性NPC患者的临床管理和个性化治疗至关重要。
