模型参数
包含肺结节风险分层成本效益分析决策模型参数的数据集。
基本信息
资源简介
模型参数
【数据集背景】
本数据集源站标题为《Model Parameters.》，为 Model Parameters. 的组成部分。源站首次发布：2026-03-05。
【数据内容】
源站原始描述：Background Artificial intelligence-based radiomic approaches have been shown to accurately evaluate indeterminate pulmonary nodules. With the expansion of lung cancer screening and utilization of computed tomography imaging, indeterminate pulmonary nodules requiring diagnostic evaluation are increasingly common. Accurate non-invasive characterization may reduce time to cancer diagnosis and decrease invasive procedures for benign disease, but the cost-effectiveness of AI-based methods has not bee…
【数据结构与技术规格】
文件数 1 个，合计 13.82 KB。Table 1.xls：application/vnd.ms-excel，13.82 KB。数据集 DOI：10.1371/journal.pone.0343492.t001。
【主题与分类】
源站学科分类：Medicine、Pharmacology、Biotechnology、Cancer。主题标签：old operative candidate、decrease invasive procedures、computed tomography imaging、assisted risk stratification、lung cancer screening、cancer risk stratification、probabilistic sensitivity analysis、given clinical variability、based radiomic approaches、ai support exceeded。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/_p_Model_Parameters_p_/31548885
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Godfrey, Caroline M.; Leech, Ashley A.; McGann, Kevin C.; Zhu, Jinyi; Marmor, Hannah N.; Pena, Sophia; et al. (2026). Model Parameters.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：<p>Model Parameters.</p>
- DOI：10.1371/journal.pone.0343492.t001
- 发布日期：2026-03-05
- 文件数：1 个
- 文件总体积：13.82 KB
- 源站页面：https://plos.figshare.com/articles/dataset/_p_Model_Parameters_p_/31548885
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/62448219"
文件清单
Table 1.xls（13.82 KB）
文件总体积 13.82 KB（源站 API 实测）。
数据说明
Background
Artificial intelligence-based radiomic approaches have been shown to accurately evaluate indeterminate pulmonary nodules. With the expansion of lung cancer screening and utilization of computed tomography imaging, indeterminate pulmonary nodules requiring diagnostic evaluation are increasingly common. Accurate non-invasive characterization may reduce time to cancer diagnosis and decrease invasive procedures for benign disease, but the cost-effectiveness of AI-based methods has not been quantified. We sought to evaluate the cost-effectiveness of AI-assisted clinician evaluation compared to clinician evaluation alone for the cancer risk stratification of patients with indeterminate pulmonary nodules.
Methods
We constructed a decision model assuming guideline-based care from a payer perspective with a lifetime horizon. The base case is a 1.1 cm incidentally discovered IPN in a 60-year-old operative candidate in a clinical population with a 65% malignancy prevalence. Cost per life-year gained (LYG) was the primary outcome. We conducted deterministic sensitivity analyses on all parameters and performed a probabilistic sensitivity analysis. Given clinical variability of malignancy prevalence, we assessed the malignancy prevalence threshold at which utilization of AI would be cost-effective.
Results
AI-supported clinician risk stratification resulted in an increase of 0.03 life years compared to clinician alone. With a 65% malignancy prevalence, AI was cost-effective with an incremental cost-effectiveness ratio (ICER) of $4,485/LYG. When the malignancy prevalence was Conclusions
In clinical settings with a pre-test probability of malignancy exceeding 5%, AI-supported IPN risk stratification is cost-effective compared to clinician assessment alone.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0343492.t001（source: https://plos.figshare.com/articles/dataset/_p_Model_Parameters_p_/31548885）。
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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