TCGA-HNSSC临床与突变组合数据集
TCGA-HNSCC头颈部鳞状细胞癌临床与突变组合数据，用于基于模糊逻辑的生存分析和风险预测，共492例患者。
基本信息
资源简介
TCGA-HNSSC临床与突变组合数据集
【数据集背景】
本数据集源站标题为《TCGA-HNSSC Clinical and Mutation combination》。源站首次发布：2025-05-13。
【数据内容】
源站原始描述：Head and neck squamous cell carcinoma (HNSCC) poses considerable clinical challenges due to its involvement with critical tissues in the head and neck. Despite a high five-year survival rate of 68.5%, it remains a highly prevalent cancer worldwide, necessitating enhanced risk assessment and individualized treatment strategies. This study leverages missense variant data from 492 HNSCC patients in The Cancer Genome Atlas to address these needs. To facilitate a more nuanced approach to personalized…
【数据结构与技术规格】
文件数 1 个，合计 48.68 MB。数据集 DOI：10.6084/m9.figshare.29046284.v1。
【主题与分类】
源站学科分类：Otorhinolaryngology。主题标签：HNSCC patients.ConclusionsOur study。
【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://figshare.com/articles/dataset/TCGA-HNSSC_Clinical_and_Mutation_combination/29046284
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Tin Ho, Cheung (2025). TCGA-HNSSC Clinical and Mutation combination. figshare. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：TCGA-HNSSC Clinical and Mutation combination
- DOI：10.6084/m9.figshare.29046284.v1
- 发布日期：2025-05-13
- 文件数：1 个
- 文件总体积：48.68 MB
- 源站页面：https://figshare.com/articles/dataset/TCGA-HNSSC_Clinical_and_Mutation_combination/29046284
命令行下载
curl -L -o "combination.xlsx" "https://ndownloader.figshare.com/files/54489821"
文件清单
combination.xlsx（48.68 MB）
文件总体积 48.68 MB（源站 API 实测）。
数据说明
Head and neck squamous cell carcinoma (HNSCC) poses considerable clinical challenges due to its involvement with critical tissues in the head and neck. Despite a high five-year survival rate of 68.5%, it remains a highly prevalent cancer worldwide, necessitating enhanced risk assessment and individualized treatment strategies. This study leverages missense variant data from 492 HNSCC patients in The Cancer Genome Atlas to address these needs. To facilitate a more nuanced approach to personalized risk assessment, we employed a fuzzy logic-based methodology to integrate results from multiple survival analyses. The proposed model combines a Fuzzy Inference System, Convolutional Neural Networks, Random Survival Forests, and Cox Proportional Hazards regression models to identify and evaluate risk factors. We evaluated the model in terms of accuracy, sensitivity, specificity, F1 score, and time-dependent area under the curve. The fuzzy-based survival analysis model had an average accuracy of 92.04%, effectively classifying and identifying missense variants linked to HNSCC mortality. By integrating diverse survival analyses, this study advances personalized medicine in HNSCC, offering valuable insights that could enhance clinical decision-making, optimize treatment strategies, and improve patient outcomes.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.29046284.v1（source: https://figshare.com/articles/dataset/TCGA-HNSSC_Clinical_and_Mutation_combination/29046284）。
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。
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