Zhichao Zuo, Jie Ma, Mi Yan, Wu Ge, Ting Yao, Lu Zhou, Ying Zeng, Yang Liu
{"title":"Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.","authors":"Zhichao Zuo, Jie Ma, Mi Yan, Wu Ge, Ting Yao, Lu Zhou, Ying Zeng, Yang Liu","doi":"10.1002/hed.27895","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":55072,"journal":{"name":"Head and Neck-Journal for the Sciences and Specialties of the Head and Neck","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Head and Neck-Journal for the Sciences and Specialties of the Head and Neck","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/hed.27895","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
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.
期刊介绍:
Head & Neck is an international multidisciplinary publication of original contributions concerning the diagnosis and management of diseases of the head and neck. This area involves the overlapping interests and expertise of several surgical and medical specialties, including general surgery, neurosurgery, otolaryngology, plastic surgery, oral surgery, dermatology, ophthalmology, pathology, radiotherapy, medical oncology, and the corresponding basic sciences.