دورية أكاديمية
أعرض في EDS

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

التفاصيل البيبلوغرافية
العنوان: Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.
المؤلفون: Li Y; Heilongjiang University of Chinese Medicine, Harbin, China., Xiao Q; Zhejiang Chinese Medical University, Zhejiang, China., Chen H; Department of Oncology, Dongying District Hospital, Dongying, Shandong, China., Zhu E; School of Medicine, Tongji University, Shanghai, China., Wang X; College of Electronic and Information Engineering, Tongji University, Shanghai, China., Dai J; School of Medicine, Tongji University, Shanghai, China., Zhang X; School of Medicine, Tongji University, Shanghai, China., Lu Q; School of Medicine, Tongji University, Shanghai, China., Zhu Y; School of Medicine, Tongji University, Shanghai, China., Yang G; Department of Oncology, Dongying District Hospital, Dongying, Shandong, China.
المصدر: Medicine [Medicine (Baltimore)] 2024 Sep 13; Vol. 103 (37), pp. e39659.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 2985248R Publication Model: Print Cited Medium: Internet ISSN: 1536-5964 (Electronic) Linking ISSN: 00257974 NLM ISO Abbreviation: Medicine (Baltimore) Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Hagerstown, Md : Lippincott Williams & Wilkins
مواضيع طبية MeSH: Squamous Cell Carcinoma of Head and Neck*/therapy , Squamous Cell Carcinoma of Head and Neck*/mortality , Deep Learning* , Head and Neck Neoplasms*/therapy , Head and Neck Neoplasms*/mortality, Humans ; Male ; Female ; Aged ; Aged, 80 and over ; Precision Medicine/methods ; Chemoradiotherapy/methods ; Retrospective Studies
مستخلص: To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for elderly head and neck squamous cell carcinoma (HNSCC) patients who are not surgery candidates. A comparison was made between patients whose treatments aligned with model recommendations and those whose did not, using overall survival as the primary metric. Bias was addressed through inverse probability treatment weighting (IPTW), and the impact of patient characteristics on treatment choice was analyzed via mixed-effects regression. Four thousand two hundred seventy-six elderly HNSCC patients in total met the inclusion criteria. Self-Normalizing Balanced individual treatment effect for survival data model performed best in treatment recommendation (IPTW-adjusted hazard ratio: 0.74, 95% confidence interval [CI], 0.63-0.87; IPTW-adjusted risk difference: 9.92%, 95% CI, 4.96-14.90; IPTW-adjusted the difference in restricted mean survival time: 16.42 months, 95% CI, 10.83-21.22), which surpassed other models and National Comprehensive Cancer Network guidelines. No survival benefit for chemoradiotherapy was seen for patients not recommended to receive this treatment. Self-Normalizing Balanced individual treatment effect for survival data model effectively identifies elderly HNSCC patients who could benefit from chemoradiotherapy, offering personalized survival predictions and treatment recommendations. The practical application will become a reality with further validation in clinical settings.
Competing Interests: The authors have no conflicts of interest to disclose.
(Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)
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تواريخ الأحداث: Date Created: 20240917 Date Completed: 20240917 Latest Revision: 20240919
رمز التحديث: 20240919
مُعرف محوري في PubMed: PMC11404971
DOI: 10.1097/MD.0000000000039659
PMID: 39287264
قاعدة البيانات: MEDLINE
الوصف
تدمد:1536-5964
DOI:10.1097/MD.0000000000039659