Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Zhichao Zuo, Jie Ma, Mi Yan, Wu Ge, Ting Yao, Lu Zhou, Ying Zeng, Yang Liu
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引用次数: 0

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).

Conclusion: 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.

非转移性鼻咽癌无进展生存期的机器学习预后特征。
背景:早期发现高风险鼻咽癌(NPC)复发至关重要。我们结合三种机器学习(ML)模型创建了机器学习衍生预后特征(MLDPS),用于预测非转移性鼻咽癌患者的无进展生存期(PFS):将653名非转移性鼻咽癌患者分为训练数据集(n = 457)和验证数据集(n = 196)(比例为7:3)。研究将临床病理特征、血液学标志物和磁共振成像结果纳入三种机器学习模型--随机森林(RF)、极梯度提升(XGBoost)和最小绝对收缩和选择算子(LASSO)--以预测无进展生存期(PFS)。维恩图确定了三种 ML 算法的重叠特征。Cox 比例危险分析确定了无进展生存期的 MLDPS:结果:RF、XGBoost 和 LASSO 算法从 33 个特征中识别出了 6 个共识因子。Cox 比例危险分析表明,MLDPS 包括年龄、淋巴细胞计数、阳性淋巴结数量和区域淋巴结密度。此外,MLDPS 还能有效地对预后进行分层,低风险个体的 PFS 优于高风险个体(P 结论:MLDPS 是一种基于临床病理学的预后分层方法:基于临床病理特征、血液学标志物和磁共振成像结果的 MLDPS 对于指导非转移性鼻咽癌患者的临床管理和个性化治疗至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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