S1 File - Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer
术前肾肿瘤风险估计数据集，包含KiTS影像、临床表格和BERT文本模型，用于风险评估与预测。
基本信息
资源简介
S1 File - Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer
【数据集背景】
配套论文《Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer》发表于 PLOS One（2025），DOI：10.1371/journal.pone.0323240。源站首次发布：2025-05-30。
【数据内容】
源站原始描述：S1 Fig. The date span of the data used for the KNIGHT Challenge. S2 Fig. Ablation study results of adjuvant therapy candidacy prediction on the validation set. S3 Fig. Future diagnosis prediction performance in the validation set as a function of training epochs. S4 Fig. Calibration curves on the test set. The average model showed the smallest Brier score. S1 Table. Percentage of missing values in the dataset. S2 Table. Sensitivity and specificity of logistic regression in the test set at Youden…
【数据结构与技术规格】
文件数 1 个，合计 122.89 KB。pone.0323240.s001.zip：application/zip，122.89 KB。数据集 DOI：10.1371/journal.pone.0323240.s001。
【主题与分类】
源站学科分类：Medicine、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Marine Biology。主题标签：sectional imaging playing、recent evidence highlights、much larger dataset、find relevant relationships、based model pretrained、accurate noninvasive tools、renal cancer prognosis、informing renal risk、training dataset consisted、adjuvant therapy based。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/S1_File_-_Preoperative_kidney_tumor_risk_estimation_with_AI_From_logistic_regression_to_transformer/29202306
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Barros, Vesna; Abdallah, Nour; Ozery-Flato, Michal; Dekel, Avihu; Raboh, Moshiko; Heller, Nicholas; et al. (2025). S1 File - Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer. 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：S1 File - Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer
- DOI：10.1371/journal.pone.0323240.s001
- 发布日期：2025-05-30
- 文件数：1 个
- 文件总体积：122.89 KB
- 源站页面：https://plos.figshare.com/articles/dataset/S1_File_-_Preoperative_kidney_tumor_risk_estimation_with_AI_From_logistic_regression_to_transformer/29202306
命令行下载
curl -L -o "pone.0323240.s001.zip" "https://ndownloader.figshare.com/files/55001529"
文件清单
pone.0323240.s001.zip（122.89 KB）
文件总体积 122.89 KB（源站 API 实测）。
数据说明
S1 Fig. The date span of the data used for the KNIGHT Challenge. S2 Fig. Ablation study results of adjuvant therapy candidacy prediction on the validation set. S3 Fig. Future diagnosis prediction performance in the validation set as a function of training epochs. S4 Fig. Calibration curves on the test set. The average model showed the smallest Brier score. S1 Table. Percentage of missing values in the dataset. S2 Table. Sensitivity and specificity of logistic regression in the test set at Youden’s J-Score operating point selected on the validation set. S3 Table. Data split of the pretraining cohort. S4 Table. Mapping of CCSR codes to 16 Charlson clinical conditions. S1 Appendix. The KiTS database. S2 Appendix. Clinical feature ablation study. S3 Appendix. Pretraining of BERT-based model for clinical records. S4 Appendix. Evaluation of top winners of KNIGHT Challenge.
(ZIP)
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0323240.s001（source: https://plos.figshare.com/articles/dataset/S1_File_-_Preoperative_kidney_tumor_risk_estimation_with_AI_From_logistic_regression_to_transformer/29202306）。
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
公开数据往往缺乏完整的病理回证与专家标注。千方医数集依托陕川双基地与超十家三甲医院直连，提供完全合规、去标识化、支持注册级质控的专病数据集交易服务。
