Machine learning in personalized laryngeal cancer management: insights into clinical characteristics, therapeutic options, and survival predictions.

IF 1.9 3区 医学 Q2 OTORHINOLARYNGOLOGY
Sakhr Alshwayyat, Tamara Feras Kamal, Tala Abdulsalam Alshwayyat, Mustafa Alshwayyat, Hamdah Hanifa, Ramez M Odat, Miassar Rawashdeh, Alia Alawneh, Kholoud Qassem
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引用次数: 0

Abstract

Purpose: Over the last 40 years, there has been an unusual trend where, even though there are more varied treatments, survival rates have not improved much. Our study used survival analysis and machine learning (ML) to investigate this odd situation and to improve prediction methods for treating non-metastatic LSCC.

Methods: The surveillance, epidemiology and end results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables for patients with non-metastatic LSCC, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict 5-year survival. A method of validation that incorporated the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using Kaplan Meier (K-M) survival analysis.

Results: The study included 63,324 patients, of whom 40,824 were diagnosed with glottic cancer (GC), 21,774 with supraglottic (SuGC) and 726 with subglottic (SC). ML models identified age, stage, and tumor size as the most important factors that affect survival. For SuGC, age, stage, and sex and stage and race for SC. In terms of treatment, best survival therapeutic options for GC and SC were surgery and radiotherapy (RT), whereas SuGC surgery only.

Conclusion: This study underscores the critical role of individualized factors in non-metastatic LSCC management, with surgery often combined with radiotherapy as the optimal treatment for early stage tumors. Despite advancements, stable prognosis highlights the need for continuous refinement of therapeutic strategies to balance tumor control and quality of life.

个性化喉癌管理中的机器学习:对临床特征、治疗选择和生存预测的见解。
目的:在过去的40年里,出现了一种不同寻常的趋势,即使有了更多不同的治疗方法,生存率并没有提高多少。我们的研究使用生存分析和机器学习(ML)来研究这种奇怪的情况,并改进治疗非转移性LSCC的预测方法。方法:监测、流行病学和最终结果(SEER)数据库为本研究的分析提供数据。为了确定非转移性LSCC患者的预后变量,我们进行了Cox回归分析,并使用五种ML算法构建了预后模型来预测5年生存率。采用纳入受试者工作特征(ROC)曲线下面积(AUC)的验证方法验证ML模型的准确性和可靠性。我们还使用Kaplan Meier (K-M)生存分析研究了多种治疗方案的作用。结果:该研究纳入63324例患者,其中40824例诊断为声门癌(GC), 21774例诊断为声门上癌(SuGC), 726例诊断为声门下癌(SC)。ML模型确定年龄、分期和肿瘤大小是影响生存的最重要因素。对于SuGC,年龄,分期,性别,分期和种族。就治疗而言,GC和SC的最佳生存治疗选择是手术和放疗(RT),而SuGC仅手术。结论:本研究强调了个体化因素在非转移性LSCC治疗中的关键作用,手术通常联合放疗是早期肿瘤的最佳治疗方法。尽管取得了进展,但稳定的预后表明需要不断改进治疗策略以平衡肿瘤控制和生活质量。
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
CiteScore
5.30
自引率
7.70%
发文量
537
审稿时长
2-4 weeks
期刊介绍: Official Journal of European Union of Medical Specialists – ORL Section and Board Official Journal of Confederation of European Oto-Rhino-Laryngology Head and Neck Surgery "European Archives of Oto-Rhino-Laryngology" publishes original clinical reports and clinically relevant experimental studies, as well as short communications presenting new results of special interest. With peer review by a respected international editorial board and prompt English-language publication, the journal provides rapid dissemination of information by authors from around the world. This particular feature makes it the journal of choice for readers who want to be informed about the continuing state of the art concerning basic sciences and the diagnosis and management of diseases of the head and neck on an international level. European Archives of Oto-Rhino-Laryngology was founded in 1864 as "Archiv für Ohrenheilkunde" by A. von Tröltsch, A. Politzer and H. Schwartze.
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