基于深度学习的局部晚期头颈部鳞状细胞癌个体化治疗推荐（表1）
局部晚期头颈部鳞状细胞癌深度学习个体化治疗推荐研究的表1文档。
基本信息
资源简介
基于深度学习的局部晚期头颈部鳞状细胞癌个体化治疗推荐（表1）
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 14.15 KB，主题涵盖 head and neck squamous cell carcinoma、chemoradiotherapy、deep learning、causal inference、precise medicine，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Table 1_Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning.docx》。配套论文《Head and neck squamous cell carcinoma》发表于 Nature Reviews Disease Primers（2020），DOI：10.1038/s41572-020-00224-3。源站首次发布：2025-01-06。
【数据内容】
源站原始描述：BackgroundThe conventional treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is surgery; however, the efficacy of definitive chemoradiotherapy (CRT) remains controversial. ObjectiveThis study aimed to evaluate the ability of deep learning (DL) models to identify patients with LA-HNSCC who can achieve organ preservation through definitive CRT and provide individualized adjuvant treatment recommendations for patients who are better suited for surgery. MethodsFive mode…
【数据结构与技术规格】
文件数 1 个，合计 14.15 KB。Table 1_Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，14.15 KB。数据集 DOI：10.3389/fmed.2024.1478842.s001。
【主题与分类】
源站学科分类：Foetal Development and Medicine。主题标签：head and neck squamous cell carcinoma、chemoradiotherapy、deep learning、causal inference、precise medicine。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Individualized_treatment_recommendations_for_patients_with_locally_advanced_head_and_neck_squamous_cell_carcinoma_utilizing_deep_learning_docx/28143005
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Zhang, Linmei; Zhu, Enzhao; Shi, Jiaying; Wu, Xiao; Cao, Shaokang; Huang, Sining; et al. (2025). Table 1_Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning.docx. Frontiers. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Table 1_Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning.docx
- DOI：10.3389/fmed.2024.1478842.s001
- 发布日期：2025-01-06
- 文件数：1 个
- 文件总体积：14.15 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Individualized_treatment_recommendations_for_patients_with_locally_advanced_head_and_neck_squamous_cell_carcinoma_utilizing_deep_learning_docx/28143005
命令行下载
curl -L -o "Table 1_Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning.docx" "https://ndownloader.figshare.com/files/51499235"
文件清单
Table 1_Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning.docx（14.15 KB）
文件总体积 14.15 KB（源站 API 实测）。
数据说明
BackgroundThe conventional treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is surgery; however, the efficacy of definitive chemoradiotherapy (CRT) remains controversial.
ObjectiveThis study aimed to evaluate the ability of deep learning (DL) models to identify patients with LA-HNSCC who can achieve organ preservation through definitive CRT and provide individualized adjuvant treatment recommendations for patients who are better suited for surgery.
MethodsFive models were developed for treatment recommendations. Their performance was assessed by comparing the difference in overall survival rates between patients whose actual treatments aligned with the model recommendations and those whose treatments did not. Inverse probability treatment weighting (IPTW) was employed to reduce bias. The effect of the characteristics on treatment plan selection was quantified through causal inference.
ResultsA total of 7,376 patients with LA-HNSCC were enrolled. Balanced Individual Treatment Effect for Survival data (BITES) demonstrated superior performance in both the CRT recommendation (IPTW-adjusted hazard ratio (HR): 0.84, 95% confidence interval (CI), 0.72–0.98) and the adjuvant therapy recommendation (IPTW-adjusted HR: 0.77, 95% CI, 0.61–0.85), outperforming other models and the National Comprehensive Cancer Network guidelines (IPTW-adjusted HR: 0.87, 95% CI, 0.73–0.96).
ConclusionBITES can identify the most suitable treatment option for an individual patient from the three most common treatment options. DL models facilitate the establishment of a valid and reliable treatment recommendation system supported by quantitative evidence.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fmed.2024.1478842.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Individualized_treatment_recommendations_for_patients_with_locally_advanced_head_and_neck_squamous_cell_carcinoma_utilizing_deep_learning_docx/28143005）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
