表1：使用人工智能神经网络评估肺鳞状细胞癌免疫治疗疗效
包含289例肺鳞状细胞癌患者临床信息，用于构建神经网络模型评估免疫治疗疗效的表格数据集。
基本信息
资源简介
表1：使用人工智能神经网络评估肺鳞状细胞癌免疫治疗疗效
【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 13.54 KB，主题涵盖 immunotherapy、lung squamous carcinoma、neural network、deep learning、predictive model，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_Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.docx》。配套论文《ALK/EML4 Fusion Gene May Be Found in Pure Squamous Carcinoma of the Lung》发表于 Journal of Thoracic Oncology（2014），DOI：10.1097/JTO.0000000000000109。源站首次发布：2022-11-28。
【数据内容】
源站原始描述：BackgroundAt present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy. MethodsIn our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training co…
中文译文（机器翻译，仅供快速理解，请以原文为准）：背景目前，免疫治疗是肺癌患者非常有前景的治疗方法，但影响反应的因素仍存在争议。预测接受免疫治疗的肺鳞癌患者的疗效至关重要。 方法在我们的回顾性研究中，我们纳入了2017年1月至2021年11月在北京胸科医院接受免疫治疗的肺鳞癌患者。所有患者被随机分为两个队列，训练队列（占总数的80%）和测试队列（占总数的20%）。训练队列用于基于临床信息建立神经网络模型，以评估肺鳞癌免疫治疗的疗效和结局。主要结局是疾病控制率（DCR），次要结局是客观缓解率（ORR）、无进展生存期（PFS）和总生存期（OS）。 结果本研究共纳入289例患者。DCR模型在内部验证中的受试者工作特征曲线下面积（AUC）值为0.9526（95%CI，0.9088–0.9879），在外部验证中为0.9491（95%CI，0.8704–1.0000）。ORR模型在内部验证中的AUC为0.8030（95%CI，0.7437–0.8545），在外部验证中为0.7040（95%CI，0.5457–0.8379）。PFS模型在内部验证中的AUC为0.8531（95%CI，0.8024–0.8975），在外部验证中为0.7602（95%CI，0.6236–0.8733）。OS模型在内部验证中的AUC为0.8006（95%CI，0.7995–0.8017），在外部验证中为0.7382（95%CI，0.7366–0.7398）。 结论神经网络模型在肺鳞癌患者免疫治疗疗效评估中显示出益处，尤其是DCR和ORR模型。在我们的回顾性研究中，我们发现新辅助和辅助免疫治疗可能为患者带来更大的疗效获益。
【数据结构与技术规格】
文件数 1 个，合计 13.54 KB。Table_1_Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，13.54 KB。数据集 DOI：10.3389/fimmu.2022.1024707.s006。
【主题与分类】
源站学科分类：Transplantation Immunology、Tumour Immunology、Immunology not elsewhere classified、Immunology、Veterinary Immunology、Animal Immunology。主题标签：immunotherapy、lung squamous carcinoma、neural network、deep learning、predictive model。
【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_Assessing_the_efficacy_of_immunotherapy_in_lung_squamous_carcinoma_using_artificial_intelligence_neural_network_docx/21627218
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Li, Siqi; Li, Wei; Ma, Tianyu; Fu, Siyun; Gao, Xiang; Qin, Na; et al. (2022). Table_1_Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.docx. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-21，引用时请注意版本时效。
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使用方式
数据集获取（Figshare）
- 源站标题：Table_1_Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.docx
- DOI：10.3389/fimmu.2022.1024707.s006
- 发布日期：2022-11-28
- 文件数：1 个
- 文件总体积：13.54 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Assessing_the_efficacy_of_immunotherapy_in_lung_squamous_carcinoma_using_artificial_intelligence_neural_network_docx/21627218
命令行下载
curl -L -o "Table_1_Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.docx" "https://ndownloader.figshare.com/files/38341166"
文件清单
Table_1_Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.docx（13.54 KB）
文件总体积 13.54 KB（源站 API 实测）。
数据说明
BackgroundAt present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy.
MethodsIn our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).
ResultsA total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088–0.9879) in internal validation and 0.9491 (95%CI, 0.8704–1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437–0.8545) in internal validation and 0.7040 (95%CI, 0.5457–0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024–0.8975) in internal validation and 0.7602 (95%CI, 0.6236–0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995–0.8017) in internal validation and 0.7382 (95%CI, 0.7366–0.7398) in external validation.
ConclusionsThe neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fimmu.2022.1024707.s006（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Assessing_the_efficacy_of_immunotherapy_in_lung_squamous_carcinoma_using_artificial_intelligence_neural_network_docx/21627218）。
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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